Radio Technologies and Concepts for IMT-Advanced

RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED Edited by Martin D...

Author: Martin Dottling | Werner Mohr | Afif Osseiran

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RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED

RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED Edited by

Martin Döttling, Nokia Siemens Networks, Germany Werner Mohr, Nokia Siemens Networks, Germany Afif Osseiran, Ericsson Research, Sweden

A John Wiley and Sons, Ltd., Publication

C 2009 Copyright

Martin Döttling, Werner Mohr, Afif Osseiran

Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770620. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trade-marks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the under-standing that the publisher is not engaged in rendering professional services. If professional ad-vice or other expert assistance is required, the services of a competent professional should be sought. Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA Wiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1 Wiley also publishes its books in a variety of electronic formats. Some of the content that appears in print may not be available in electronic books. Library of Congress Cataloging-in-Publication Data Döttling, Martin. Radio technologies and concepts for IMT-Advanced / Martin Döttling, Werner Mohr, and Afif Osseiran. p. cm. Includes bibliographical references and index. ISBN 978-0-470-74763-6 (H/B) 1. Radio—Transmitter-receivers—Standards. 2. Cellular telephones—Standards. 3. Radio—Receivers and reception—Technological innovations. 4. Global system for mobile communications— Equipment and supplies— Standards. I. Mohr, Werner, 1955– II. Osseiran, Afif. III. Title. TK6562.P32D68 2009 621.384—dc22 2009007658 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Typeset in 10/12pt Times by Aptara Inc., New Delhi, India Printed in Singapore by Markono

To my family and parents Martin Döttling To my parents Werner Mohr In memory of my parents, Hayat and Hani Afif Osseiran

Contents About the Editors

xxi

Preface

xxv

Acknowledgements

xxix

Abbreviations

xxxi

List of Contributors

xliii

1 1.1 1.2 1.3 1.4 1.5 1.6

Introduction Development and Status of Mobile and Wireless Communications Expectations of Data Traffic Growth Development Towards IMT-Advanced Global Research Activities WINNER Project Future Work References

2 Usage Scenarios and Technical Requirements 2.1 Introduction 2.2 Key Scenario Elements 2.2.1 Environment Type and Coverage Range 2.2.2 Terminal Type 2.2.3 User Density and Traffic Parameters 2.2.4 User Mobility 2.2.5 Deployment Scenarios 2.2.5.1 Wide Area 2.2.5.2 Metropolitan Area 2.2.5.3 Local Area 2.3 Service Classes and Service Requirements 2.3.1 Overview of Beyond-3G Applications 2.3.2 Requirements for Service Provisioning 2.3.3 Mapping of Service Requirements to RAN Requirements 2.3.4 Traffic Models 2.3.4.1 Internet Applications 2.3.4.2 Voice over IP

1 1 3 4 6 8 9 10 13 13 13 15 15 16 16 18 18 19 19 20 20 20 20 20 22 23

viii

2.4

2.5 2.6

2.7

2.8 2.9

Contents

2.3.4.3 Video Telephony 2.3.4.4 Streaming 2.3.4.5 File Transfer 2.3.4.6 Interactive Applications Requirements for System Capabilities 2.4.1 Generalised Mobility Support within WINNER 2.4.2 Generalised Mobility Support between WINNER and Legacy Networks 2.4.3 Measurement Requirements for the WINNER System 2.4.4 Support for QoS Mechanisms and Prioritisation of Flows Terminal Requirements Performance Requirements 2.6.1 Coverage 2.6.2 Data Rate 2.6.2.1 Definition of User Throughput 2.6.2.2 Peak Data Rate 2.6.2.3 Sustainable Data Rate 2.6.3 Allowable Error Rate 2.6.4 Delay 2.6.4.1 Definition of User-Plane Packet Delay 2.6.4.2 Achievable User-Plane Packet Delay 2.6.5 Spectral Efficiency 2.6.6 Maximum Terminal Speed Spectrum Requirements 2.7.1 WINNER Spectrum Range 2.7.2 Utilisation of Current Mobile Service Bands 2.7.3 Spectrum Fragmentation 2.7.4 Coexistence with Other Systems 2.7.5 Sharing Spectrum between WINNER RANs 2.7.6 Sharing Spectrum between Cell Layers of a WINNER System 2.7.7 System Bandwidth Dependency of Requirements Conclusion Acknowledgements References

3 WINNER II Channel Models 3.1 Introduction 3.2 Modelling Considerations 3.2.1 Propagation Scenarios 3.2.1.1 A1: Indoor Office 3.2.1.2 B1: Urban Microcell 3.2.1.3 B4: Outdoor to Indoor 3.2.1.4 C1: Suburban Macrocell 3.2.1.5 C2: Urban Macrocell 3.2.1.6 D1: Rural Macrocell

23 23 24 24 24 25 25 26 28 28 29 30 30 30 31 31 31 31 31 32 32 34 34 34 34 34 35 35 35 36 36 36 37 38 39 39 40 40 41 42 43 43 43 43

Contents

3.2.1.7 B2 and C3: Bad Urban Conditions Evolution of Channel Models from 2G to 4G Selection of Channel-modelling Approach Modelling Process Network Layout Measurements 3.2.6.1 Measurement Tools 3.2.6.2 Channel Measurements Channel-Modelling Approach 3.3.1 WINNER Generic Channel Model 3.3.1.1 Modelled Parameters 3.3.1.2 Correlations Between Large-Scale Parameters 3.3.2 Channel Segments, Drops and Time Evolution 3.3.3 Nomadic Channel Condition Channel Models and Parameters 3.4.1 Applicability 3.4.1.1 Environment Dependence 3.4.1.2 Frequency Dependence 3.4.2 Generation of Channel Coefficients 3.4.3 WINNER Path-loss Models 3.4.3.1 Frequency Dependencies of WINNER Path-loss Models 3.4.3.2 Transitions Between LOS and NLOS Conditions 3.4.4 Values for Generic Channel Models Channel Model Usage 3.5.1 System-level Description 3.5.1.1 Coordinate System 3.5.1.2 Single User (Handover) Multicell Simulation 3.5.1.3 Multi-user Multicell Simulation 3.5.2 SPACE–TIME Concept in Simulations 3.5.3 Bandwidth and Frequency Dependence 3.5.3.1 Frequency Sampling 3.5.3.2 Bandwidth Downscaling in the Delay Domain 3.5.3.3 Bandwidth Downscaling in the Frequency Domain 3.5.3.4 FDD Modelling 3.5.4 Approximation of Channel Models 3.5.4.1 Reduced Complexity Models 3.5.4.2 Comparison of Complexity of Modelling Methods Conclusion Acknowledgements References

43 44 46 47 48 50 50 55 59 63 63 64 68 70 70 71 71 71 71 75 75 77 77 81 81 81 81 84 84 85 85 85 85 86 86 86 87 89 90 90

System Concept and Architecture Introduction Design Principles and Main Characteristics Logical Node Architecture

93 93 94 96

3.2.2 3.2.3 3.2.4 3.2.5 3.2.6

3.3

3.4

3.5

3.6

4 4.1 4.2 4.3

ix

x

Contents

4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 4.3.6

Overview Pool Concept and Micro Mobility Equipment Sharing Multicast and Broadcast Service Support Multiband Transmission from Different BSs Logical Nodes 4.3.6.1 Gateway Nodes: GW IPA L N and GW C L N 4.3.6.2 Base Station Node: BS L N 4.3.6.3 Relay Node: RN L N 4.3.6.4 User Terminal: UT L N 4.3.6.5 RRMserver L N 4.3.6.6 SpectrumServer L N 4.4 Protocol and Service Architecture 4.4.1 Overview 4.4.2 Layer 3: Radio Resource Control 4.4.3 Layer 2 4.4.3.1 IP Convergence Layer 4.4.3.2 Radio Link Control Layer 4.4.3.3 Medium Access Control Layer 4.4.4 Layer 1: Physical 4.4.4.1 Control Signalling 4.4.4.2 Physical Channels and Mappings to Transport Channels 4.4.4.3 Synchronisation Pilots 4.5 Conclusion Acknowledgements References

96 98 101 102 103 104 104 106 107 108 108 108 109 109 110 112 114 114 115 125 126 128 131 132 132 132

5 5.1 5.2 5.3

135 135 136 137 137 139 141 144 145 146 149 151 152 154 154 154 157 158 160

Modulation and Coding Techniques Introduction Basic Modulation and Coding Scheme Coding Schemes 5.3.1 Low-density Parity-check Codes 5.3.1.1 Encoding of BLDPC Codes 5.3.1.2 Decoding Methods 5.3.1.3 Scheduling Algorithms 5.3.1.4 Lifting Process of LDPC Codes 5.3.1.5 Rate-Compatible Puncturing Codes 5.3.1.6 SNR Mismatch Impact on LDPC Codes 5.3.2 Duo-Binary Turbo Codes 5.3.3 Low-Rate Convolutional Codes for Control Channel 5.3.4 Comparison of Coding Schemes 5.3.4.1 Performance Comparison 5.3.4.2 Performance–Complexity Trade-Off 5.3.4.3 Domain of Suitability 5.3.4.4 Implementation Issues: Flexibility, Parallelization and Throughput 5.4 Link Adaptation

Contents

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5.5 Link Level Aspects of H-ARQ 5.5.1 Incremental Redundancy Scheme 5.5.2 Throughput and Delay Analysis 5.6 Conclusions References

162 162 163 165 166

6 Link Level Procedures 6.1 Introduction 6.2 Pilot Design 6.2.1 Types of Pilot 6.2.2 Reference Pilot Design 6.2.2.1 In-band Pilot Patterns 6.2.2.2 Uplink Super-Frame Pilot Preamble 6.2.2.3 Case Study for the Reference Pilot Design 6.2.3 Capacity-Achieving Pilot Design 6.3 Channel Estimation 6.3.1 Channel Estimation Reference Design 6.3.2 Pilot-Aided Channel Estimation 6.3.3 Iterative Channel Estimation 6.3.3.1 Channel Estimation for Single-Input, Single-Output Scenarios 6.3.3.2 Channel Estimation for Multiple-Input, Multiple-Output Scenarios 6.3.4 Channel Prediction 6.4 Radio Frequency Impairments 6.4.1 HPA Non-Linearities 6.4.2 Phase Noise 6.4.2.1 Phase Noise Model 6.4.2.2 Phase Noise Suppression in OFDM with Spatial Multiplexing 6.4.2.3 Phase Noise Suppression for DFT-Precoded OFDM (Serial Modulation) 6.5 Measurements and Signalling 6.6 Link Level Synchronisation 6.6.1 Synchronisation Preamble Design 6.6.2 Synchronisation in a Licensed Band 6.6.2.1 Coarse Symbol Timing Synchronisation 6.6.2.2 Frequency Offset Estimation 6.6.3 Synchronisation in Shared Spectrum 6.7 Network Synchronisation 6.7.1 Firefly Synchronisation 6.7.1.1 Mathematical Model 6.7.1.2 Synchronisation of Coupled Oscillators 6.7.1.3 Refractory Period 6.7.2 Synchronisation Rules 6.7.3 Compensating for Propagation Delays: Timing Advance 6.7.4 Imposing a Global Time Reference on Firefly Synchronisation 6.8 Conclusion 6.8.1 Pilot Design 6.8.2 Channel Estimation

169 169 169 171 172 172 177 177 179 179 180 181 182 182 185 190 192 192 195 196 196 198 200 201 201 202 202 203 204 205 205 206 206 207 207 209 210 211 211 211

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Contents

6.8.3 RF Imperfections 6.8.4 Link Layer Synchronisation 6.8.5 Self-Organised Network Synchronisation Acknowledgements References 7 Advanced Antennas Concept for 4G 7.1 Introduction 7.2 Multiple Antennas Concept 7.2.1 Generic Transmitter 7.2.1.1 Per Stream Rate Control 7.2.1.2 Space–Time Block Code 7.2.1.3 SDMA 7.2.2 Control Signalling 7.3 Spatial Adaptation 7.3.1 Single Stream Per User 7.3.2 Multiple Streams Per User 7.4 Spatial Schemes 7.4.1 Receive Diversity 7.4.2 Beamforming 7.4.2.1 Signal Model 7.4.2.2 Results 7.4.3 Diversity and Spatial Multiplexing 7.4.4 Beamforming and Spatial Multiplexing 7.4.4.1 Clustered Array Structure 7.4.4.2 Results 7.4.5 Linear MU-MIMO: SMMSE and RBD 7.4.5.1 System Models 7.4.5.2 Results 7.5 Interference Mitigation 7.6 Pilots, Feedback and Measurements 7.6.1 Pilots 7.6.2 Feedback 7.6.3 Measurements 7.7 MIMO Aspects in Relaying 7.7.1 Cooperative Relaying 7.7.1.1 Cooperative Diversity Relaying 7.7.1.2 Two-Dimensional Cyclic Prefix 7.7.2 Distributed Antenna Systems 7.7.2.1 Distributed MIMO Configuration 7.7.2.2 Performance of Linear MU-MIMO Precoding 7.8 Conclusion 7.8.1 Beamforming 7.8.2 Diversity and Linear Dispersion Codes 7.8.3 Multi-User MIMO Precoding

212 212 212 213 213 219 219 221 221 226 227 228 228 229 230 231 231 231 232 233 235 237 241 243 243 247 249 250 250 253 253 255 257 258 260 261 262 264 265 266 269 269 270 271

Contents

7.8.4 Distributed Antenna Systems and Cooperative Relaying Acknowledgements References 8 Layer-2 Relays for IMT-Advanced Cellular Networks 8.1 Introduction 8.1.1 Rationale for Relays in Cellular Networks 8.1.2 Organization of this Chapter 8.2 Motivation for Layer-2 Relays and Prior Work 8.3 Relay-based Deployments 8.3.1 RN Deployment Concepts 8.3.1.1 Relaying for Coverage Improvement 8.3.1.2 Relaying for Capacity Optimization at Outer Cell Regions 8.3.1.3 Relaying to Cover Shadowed Areas 8.3.2 Sub-cell Capacity of a Relay-enhanced Cell 8.3.2.1 Multi-hop Throughput in Cellular Deployment 8.3.2.2 Sub-cell Capacity Served by an RN 8.3.2.3 Capacity of a Multi-hop Connection under Delay Constraint 8.3.3 WINNER Test Scenarios 8.3.3.1 Base Urban Coverage Test Scenario 8.3.3.2 Metropolitan Area Test Scenario 8.3.4 Cost Efficiency of RNs 8.4 Design Choices for Relay-based Cellular Networks 8.4.1 Half-duplex Saves Costs and Improves Deployment Flexibility 8.4.2 Decode-and-Forward Relaying Exploits Adaptive Modulation and Coding 8.4.3 Fixed Relays in MCN Assist Fast and Cheap Network Roll-out 8.4.4 Flexible Radio Resource Management Adapts to the Environment 8.4.4.1 Static Load-based Resource Partitioning 8.4.4.2 Dynamic-resource Sharing in Wide Area Deployment with Beamforming 8.4.4.3 Soft Frequency Re-use and Static Load-based Resource Partitioning 8.4.5 MIMO Techniques Boost Capacity 8.4.6 Cooperative Relaying Boosts Performance 8.5 System and Network Aspects 8.5.1 Relaying by the WINNER MAC Protocol 8.5.2 Cell Broadcast and Resource Allocation 8.5.3 Radio Resource Partitioning 8.5.4 Relay ARQ 8.6 System-level Performance Evaluation 8.6.1 Scenario and Traffic Modelling 8.6.2 System Model 8.6.3 Resource Partitioning 8.6.4 Uplink Power Control and Resource Allocation 8.6.5 Simulation Results 8.6.5.1 Baseline Resource Partitioning 8.6.5.2 Downlink Performance of Infinite Buffer and Optimum Resource Partitioning

xiii

271 271 271 277 277 277 280 280 282 283 284 285 285 286 287 287 289 291 291 292 293 295 296 296 296 297 299 300 302 302 304 306 308 308 310 311 312 312 313 315 316 317 318 319

xiv

Contents

8.7 Conclusion Acknowledgements References

319 321 321

9 9.1 9.2

325 325 326 326 326 327 328 328 328 329 330 332

9.3

9.4

Multiple Access Schemes and Inter-cell Interference Mitigation Techniques Introduction Multiple Access Schemes 9.2.1 Classic Multiple Access Schemes 9.2.1.1 Frequency Division Multiple Access 9.2.1.2 Time Division Multiple Access 9.2.1.3 Code Division Multiple Access 9.2.2 Multi-carrier Multiple Access Schemes 9.2.2.1 Orthogonal Frequency Division Multiple Access 9.2.2.2 Multi-Carrier Code Division Multiple Access 9.2.3 WINNER Multiple Access and Medium Access Control Concept 9.2.3.1 Chunk-wise Adaptive TDMA/OFDMA 9.2.3.2 Block Interleaved and Block Equidistant Frequency Division Multiple Access 9.2.3.3 Configuration of Non-Frequency-Adaptive Multiple Access Schemes 9.2.3.4 Co-existence and Switching 9.2.4 MAC Transmission Control 9.2.4.1 Transmission Control Sequences for Downlinks 9.2.4.2 Transmission Control Sequences for Uplinks 9.2.4.3 Transmission and Retransmission Delays Inter-cell Interference Mitigation Schemes 9.3.1 Modelling Inter-cell Interference 9.3.1.1 Link-Level Model 9.3.1.2 System-Level Model 9.3.2 Inter-cell Interference Averaging Techniques 9.3.2.1 Inter-cell Interference Cancellation 9.3.2.2 Dynamic Channel Allocation and Scheduling 9.3.3 Inter-cell Interference Avoidance Techniques 9.3.3.1 Resource Management by Restriction of Transmit Power 9.3.3.2 Self-adaptive Re-use Partitioning 9.3.3.3 Cost-function-based Scheduling 9.3.3.4 Simulation Results 9.3.4 Inter-cell Interference Mitigation Techniques Based on Smart Antennas 9.3.4.1 Beamforming Techniques 9.3.4.2 Transmit Diversity Techniques 9.3.4.3 Receive Diversity and Interference Suppression Techniques 9.3.4.4 Simulation Results Conclusion Acknowledgements References

336 340 343 346 346 347 348 349 350 350 351 351 352 357 360 360 362 363 364 365 365 368 370 370 372 373 373

Contents

10 Radio Resource Control and System Level Functions 10.1 Introduction 10.2 IPCL Layer 10.2.1 Transfer of User Data Between IPCL Entities 10.2.1.1 IPCL Header Compression 10.2.1.2 IPCL Data Ciphering and Ciphering Keys 10.2.2 IPCL and Handover 10.2.2.1 In-Sequence Delivery of Upper Layer PDUs 10.2.2.2 Duplicate Detection of Lower Layer SDUs 10.3 Radio Resource Control 10.3.1 RRC States 10.3.1.1 UT Detached State 10.3.1.2 UT Idle State 10.3.1.3 UT Active State 10.3.2 Mobility Management in Idle Mode 10.3.2.1 Paging 10.3.2.2 Tracking Area 10.3.3 Mobility Management in Active Mode 10.3.3.1 Micro Mobility 10.3.3.2 Macro Mobility 10.3.3.3 Intramode Handover 10.3.3.4 Intermode Handover 10.3.3.5 Intersystem Handover 10.3.3.6 Inter GW Handover and Load Balancing 10.3.4 Flow Admission Control 10.3.5 Congestion Avoidance Control 10.3.5.1 Admission Control: Two-Stage Approach 10.3.5.2 Flow Control 10.3.6 Load and Congestion Control 10.4 Centralised, Distributed and Hybrid RRM Architecture 10.4.1 Distributed RRM 10.4.2 Centralised RRM 10.4.3 Hybrid RRM 10.5 System-Level Performance Results 10.5.1 Intersystem Handover 10.5.2 Intermode Handover 10.5.2.1 Simulation Setup 10.5.2.2 Intramode and Intermode Handover Algorithms 10.5.3 Intermode Handover Results 10.5.3.1 Intermode Handover Triggered by Residual Throughput 10.5.3.2 Intermode Handover Triggered by UT Velocity 10.6 Conclusion Acknowledgements References

xv

377 377 378 378 379 380 381 382 382 383 383 383 384 384 385 385 385 386 386 388 389 390 392 393 394 396 396 401 404 406 406 406 407 407 407 409 409 410 412 412 414 414 415 416

xvi

11 Sharing and Flexible Spectrum Use Capabilities 11.1 Introduction 11.2 Spectrum Technologies Framework 11.2.1 Sharing and Co-existence Functions 11.2.1.1 Vertical Sharing 1: WINNER Is the Primary System 11.2.1.2 Vertical Sharing 2: WINNER Is the Secondary System 11.2.1.3 Horizontal Sharing with Coordination 11.2.1.4 Horizontal Sharing Without Coordination 11.2.2 Spectrum Assignment Functions 11.2.2.1 Long-term Assignment 11.2.2.2 Short-term Assignment 11.2.3 Generic Spectrum Functions 11.2.3.1 WINNER Spectrum Manager 11.2.3.2 Spectrum Register 11.3 Detailed Design of a Spectrum Assignment Negotiation Mechanism 11.3.1 Long-term Spectrum Assignment 11.3.2 Short-term Spectrum Assignment 11.3.3 Interactions between Long-term and Short-term Spectrum Assignment 11.3.4 Registration of Nodes with Spectrum Manager 11.3.5 Specific Short-term Spectrum Assignment Algorithms 11.3.5.1 Negotiated Amount of Resources Exchanged 11.3.5.2 Matching Amount of Resources Exchanged 11.3.5.3 Surplus of Resources Exchanged 11.4 Spectrum Assignment Enabling Mechanisms 11.4.1 Multi-band Scheduler 11.4.1.1 Hybrid ARQ Context Transfer 11.4.1.2 MBS and Spectrum Sharing 11.4.2 Communication Between Base Stations 11.4.2.1 Trends in BS-to-BS Communication and Site Sharing 11.4.2.2 Requirements for BS-to-BS Communication 11.4.2.3 Possibilities for Inter-BS Communication 11.4.2.4 Summary of BS-to-BS Communication Technologies 11.5 WINNER Sharing with FSS 11.5.1 Dimensioning of Hard Exclusion Zones 11.5.1.1 Typical FSS Parameters Considered for the Simulation 11.5.1.2 Results 11.5.2 Mitigated Exclusion Zone Calculation 11.5.3 Advanced Mitigation Techniques 11.5.3.1 Utilisation of Information Describing the FSS Usage in a Database 11.5.3.2 Utilisation of Spectrum Beacon Channel 11.5.3.3 Multi-antenna Technologies 11.6 Performance Evaluation of Spectrum Assignment Mechanisms 11.6.1 Performance Assessment of Long-term Spectrum Assignment 11.6.1.1 Considered Scenarios 11.6.1.2 Gradual Spectral Deployment of WINNER RANs 11.6.2 Performance Assessment of Short-term Spectrum Assignment

Contents

419 419 420 421 421 421 422 422 423 423 424 424 424 424 425 425 427 429 430 430 431 431 431 431 431 434 435 435 435 436 437 440 440 442 442 442 444 445 446 446 446 447 447 447 449 451

Contents

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11.6.2.1 Evaluation of Inter-cell Interference Issues for ST Spectrum Assignment 11.6.2.2 Cell-Pair Selection Algorithms for ST Assignment 11.6.2.3 Impact of Cell-Selection Algorithms on ST Performance Assignment 11.7 Conclusion Acknowledgements References

451 453 453 455 456 456

12 ITU-R Spectrum Demand Calculation for IMT-Advanced 12.1 Introduction 12.2 ITU-R Work on Spectrum Requirements of IMT-Advanced 12.2.1 Background and Role of ITU-R 12.2.2 ITU-R Preparations for WRC-07 12.2.3 WINNER Contributions to ITU-R 12.3 ITU-R Spectrum Calculation Methodology 12.3.1 Methodology Flow and Definitions 12.3.1.1 Services 12.3.1.2 Environments 12.3.1.3 Radio Access Technique Groups 12.3.2 Traffic Calculation and Distribution 12.3.3 Capacity Requirement Calculation 12.3.4 Spectrum Requirement Calculation 12.3.5 Summary of Methodology 12.4 Software Implementation of Methodology 12.4.1 Description and Use of Software Tool 12.4.2 Inputs to Software Tool 12.4.3 Intermediate Calculations and Outputs from Software Tool 12.5 Estimated Spectrum Requirements of IMT-Advanced 12.6 Conclusion Acknowledgements References

459 459 460 460 461 463 464 464 464 467 467 468 469 470 471 472 472 474 475 477 478 479 479

13 13.1 13.2 13.3 13.4

481 481 482 483 484 485 485 489 489 489 489 492 492 493 495

System Model, Test Scenarios, and Performance Evaluation Introduction Performance Assessment of Wireless Networks Interface between Link and System Simulations Test Scenarios 13.4.1 Test Environments 13.4.2 Deployment Assumptions 13.4.2.1 Base Station 13.4.2.2 User Terminal 13.4.2.3 Relay Node 13.4.2.4 Network Layout 13.4.2.5 Channel Modelling 13.4.3 Basic OFDM Parameters and Frame Dimensions 13.5 Spectral Efficiency and Number of Satisfied Users under QoS Constraints 13.6 End-to-End Performance Evaluation

xviii

Contents

13.6.1 Base Coverage Urban Scenario 13.6.1.1 Frequency-domain Link Adaptation Gains 13.6.1.2 Spectral Efficiency and Maximum Number of Satisfied Users 13.6.1.3 Improving Cell Edge Performance and Indoor Coverage by Relaying 13.6.1.4 Dynamic Resource Allocation in Relay-Enhanced Cells 13.6.1.5 Cooperative Relaying 13.6.1.6 Multicast/Broadcast Services in Relay-Enhanced Cells 13.6.1.7 Impact of Traffic and Packet Modelling on Spectral Efficiency 13.6.2 Microcellular Scenario 13.6.2.1 Indoor Coverage Improvement by Relay Deployments 13.6.2.2 Soft Frequency Re-use 13.6.2.3 Soft and Fractional Frequency Re-use and Re-use One 13.6.3 Local Area Scenarios 13.7 Conclusion Acknowledgements References

495 495 498 501 503 504 504 508 513 513 514 516 517 521 521 522

14 Cost Assessment and Optimisation for WINNER Deployments 14.1 Introduction 14.2 Cost Assessment Framework and Assumptions 14.2.1 General Cost Assessment Procedure 14.2.2 Types of Cost Assessment 14.2.3 Challenges in Cost Assessment 14.2.3.1 Spectrum Sharing 14.2.3.2 Roaming Agreements 14.2.3.3 Infrastructure Sharing 14.2.3.4 Third-party Network Ownership, Operation and Maintenance 14.2.3.5 New Business Entities 14.2.3.6 Summary 14.2.4 WINNER: Assumptions and Technology Options 14.3 Cost Components 14.3.1 Classification of Cost Components 14.3.2 RAN CAPEX Costs 14.3.2.1 Base Station Equipment 14.3.2.2 Relay Equipment 14.3.2.3 Base Station Deployment 14.3.2.4 Relay Deployment 14.3.2.5 Base Station Site Acquisition 14.3.2.6 Relay Site Acquisition 14.3.2.7 Gateways 14.3.2.8 Centralised RRM Servers 14.3.2.9 RAN Connectivity 14.3.2.10 Initial Radio Planning and Network Optimisation 14.3.3 RAN OPEX Costs 14.3.3.1 Base Station Site Rent and Maintenance

525 525 526 526 527 528 528 529 529 529 529 530 530 530 531 532 532 533 533 534 534 534 534 535 535 535 535 536

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14.3.3.2 Relay Site Rent and Maintenance 14.3.3.3 Rent for RAN Connectivity 14.3.3.4 Power 14.3.3.5 Network Operation and Maintenance 14.3.3.6 Software and Firmware Updates 14.3.4 Example Cost Figures 14.3.4.1 Breakdown of Macro BS Equipment Costs 14.3.4.2 Transformation of OPEX into CAPEX Costs 14.4 Cost Assessment Models 14.4.1 Previous Work 14.4.2 Background and Principles 14.4.3 Network Deployment 14.4.3.1 Traffic Modelling 14.4.3.2 RAP Deployment Strategies 14.4.3.3 Radio Propagation Models 14.4.3.4 Radio and Resource Assignment Model 14.4.4 Cost Calculation 14.4.4.1 Relay-Specific Cost Evaluation Issues 14.4.4.2 Deployment Representation by Indifference Maps 14.5 Reference Deployment Scenarios and Cost Assessments 14.5.1 Deployment Simulations and Assumptions 14.5.2 Case Studies 1 and 2: WA Urban, Relay Nodes vs Micro BS 14.5.2.1 Simulation Scenario Description 14.5.2.2 Cost-optimal Deployment and Total Deployment Cost of RNs 14.5.2.3 Cost-optimal Deployment and Total Deployment Cost of Micro BSs 14.5.2.4 Incumbent vs Greenfield Deployment Costs 14.5.3 Case Studies 3 and 4: WA Rural, Relay Nodes vs Micro BS 14.5.3.1 Simulation Scenario Description 14.5.3.2 Cost-optimal Deployment and Total Deployment Cost 14.5.4 Case Studies 5 and 6: WA Urban, Relay Nodes vs Micro BS, Intelligent BS Deployment 14.5.5 Case Studies 7 and 8: MIMO Assessment 14.5.5.1 Deployment Cost Comparison between SISO and MIMO Systems 14.5.5.2 Performance Improvement from the Use of Multiple Antennas 14.5.5.3 Deployment Evaluation 14.5.5.4 Cost Assessment of the Multi-antenna Configurations 14.6 Conclusion Acknowledgements References

536 536 537 537 537 537 538 539 540 540 541 541 542 543 544 546 549 549 550 555 555 555 555 557 559 560 560 560 561 562 564 564 565 565 566 566 567 567

Index

569

About the Editors Martin Döttling Dr Martin Döttling was born in Oppenau, Germany, on May 17, 1969. He received Dipl.-Ing. (MSEE) and Dr.-Ing. (PhDEE) degrees from Universität Karlsruhe in 1995 and 2000, respectively. From 1995 to 2000, he was a research assistant at the Institut für Höchstfrequenztechnik und Elektronik (IHE), Universität Karlsruhe. His research activities included ray optical propagation modelling, mobile communications and land mobile satellite systems. At the Universität Karlsruhe and the Carl-Cranz Academy for scientific education, he was a lecturer in radio-wave propagation and radio-network planning. He participated as an expert in the European research programs COST 231, COST 255, and COST 273. From February 2001 until September 2006, he worked for Siemens AG, Mobile Communications in Munich, Germany. In October 2006, he joined Siemens Networks GmbH & Co. KG and, from April 2007 to August 2009, he has been with Nokia Siemens Networks GmbH & Co. KG (NSN). Since September 2009 he is with the European Patent Office, Munich, Germany. From 2001 to 2004, Martin Döttling worked on UMTS standardisation in the 3rd Generation Partnership Project (3GPP), focusing on the physical layer of wireless high-speed packet data transmission (HSDPA, HSUPA) covering both link- and system-level aspects. In 2005, he was responsible for the MIMO spatial-processing concept of the mobile communication system studied within the first phase of the European research project, WINNER. During 2006 and 2007, he was work package leader for the WINNER II system concept and expert for IMTAdvanced mobile communication systems research. From 2008 to 2009, he has been leading research and standardisation projects in the area of self-organising networks and contributes to the Long-Term Evolution (LTE) project of 3GPP. In 2009, he was acting as a Chief Architect for LTE. His publications include more than 90 contributions in books, journals, conferences and standardization documents. In 2004, he received the IEEE VTC 2004 Fall Best Paper Award and in 2008 he was appointed NSN Inventor of the year. During his career, he has served as a technical programme committee member, session chair, and reviewer for various international conferences, as a reviewer for international journals and has filed over 50 patent applications.

Werner Mohr Dr Werner Mohr was born in Hann. Münden, Germany, on June 2, 1955. He received a Masters degree and a PhD, both in electrical engineering, from the University of Hanover, Germany, in 1981 and 1987, respectively.

xxii

About the Editors

He worked for BEB Betriebsführungsgesellschaft – an oil and gas company – from 1981 to 1982, where he was responsible for the investigation of a measurement system. In 1982, he returned to the University of Hanover as a member of the research staff of the Institute of High-Frequency Technology. From 1987 to 1990, he was senior engineer at the same institute. From 1989 to 1990, he was a lecturer at the Fachhochschule Hanover, Germany for telecommunication systems. Werner Mohr joined Siemens AG, Mobile Network Division in Munich, Germany in 1991. He was responsible for the development of a wideband propagation measurement system, propagation measurements and channel modelling and he was involved in the European RACE-II Project ATDMA in first investigations for the third-generation mobile radio interface. Wideband propagation channel models, which were developed based on extensive measurement campaigns in the ATDMA project, were internationally standardised by ETSI SMG and ITU-R TG 8/1 for the evaluation of third-generation mobile radio interface proposals. From 1995 to 1996, Werner Mohr was active in ETSI SMG5 for standardisation of UMTS. During that time, he was also responsible for the evaluation of several mobile radio standards. He was project manager of the ACTS FRAMES Project from December 1996 until the project finished in August 1999. This project developed the basic concepts of the UMTS radio interface and the ETSI SMG decision on the UMTS radio interface (UTRA concept) was based in large part on the contributions of the ACTS FRAMES project. He has held several positions in Siemens AG, Communications Business Unit, in the research domain and the CTO Office. He was involved in the 5th Framework Programme of the EU in different projects and as project coordinator. These projects dealt with preparatory research activities towards mobile communication systems beyond the third generation or IMT-Advanced. From 2001 to 2003, he was active in ITU-R WP8F, working on the development of ITU-R Recommendation M.1645 (Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000). From 2004 to 2007, he was the coordinator of the WINNER Project in Framework Programme 6 of the European Commission and chairman of Wireless World Initiative (WWI) – a group of cooperating projects towards systems beyond 3G in Framework Programme 6. The WINNER project developed concepts and algorithms that were the basis for the IMT-Advanced radio interface. Since 2008, he has been the coordinator of the Eureka Celtic project WINNER+, which is further developing such concepts based on the outcome of the World Radiocommunication Conference (WRC) 2007. Since April 2007, he has worked for Nokia Siemens Networks GmbH & Co. KG in Munich, Germany. He is Head of Research Alliances. Werner Mohr was chair of the Wireless World Research Forum (WWRF) from its launch in August 2001 to December 2003. He is vice-chair of the eMobility European Technology Platform from 2008 to 2009. He is a member of Verband der Elektrotechnik, Elektronik und Informationstechnik (VDE) and a Senior Member of IEEE. In 1990, he received the Award of the Information Technology Society (ITG) of VDE. He is a board member of ITG in VDE from 2006 to 2011. He is listed in the US Who’s Who in the World, Who’s Who in Science and Engineering and other publications. He has published over 100 technical papers in international journals and conferences, including invited papers. He has presented tutorials and organised and participated in panel discussions on several topics such as third-generation mobile radio systems and beyond. He is co-author of Third Generation Mobile Communications Systems and has contributed to other

About the Editors

xxiii

published books. He has served as session chair in several international conferences. He has been a member of several technical programme committees of international conferences, e.g., IEEE Globecom ’99, ’01, ’02, ’06, IEEE ICC ’04 to ’06, IEEE PIMRC ’00 to ’07, IEEE VTC Fall ’99, ’02, ’03, ’05, ’06, IEEE VTC Spring ’02, IEEE WCNC ’02, ’03, ’07, WPMC ’98 to ’08, European Wireless ’99, ’00, ’02, ’05, ’06, ’09 and was Executive Chair of IEEE WCNC 2005. In addition, he was the guest editor of a special issue of IEEE Network Magazine on 4G wireless in 2006.

Afif Osseiran Afif Osseiran was born in Saida, Lebanon. He received a BSc in Electrical Engineering and Electronics from Université de Rennes 1, France, in 1995, and a DEA (postgraduate) degree in Electrical Engineering from Université de Rennes 1 and INSA Rennes in 1997, and an ´ MASc degree in Electrical and Communication Engineering from Ecole Polytechnique de Montréal, Canada, in 1999. In May 2006, he successfully defended his PhD thesis at Radio Communication Group at the Royal Institute of Technology (KTH) in Stockholm. Since 1999, he has worked for Ericsson, Sweden. In 2004, as one of Ericsson’s representatives, he joined the European project WINNER funded under the 6th Framework Programme. During 2006 and 2007, he led the spatial temporal processing (i.e. MIMO) task, which mainly deals with multiple antenna techniques for future generations. Since April 2008, he has been the technical manager of the Eureka Celtic project WINNER+. He is also the leader of the System Concept Design Work Package in WINNER+. His research interests include many aspects of wireless communications with a special emphasis on advanced antenna systems for the third generation (WCDMA, HSPA, LTE) and future generations (IMT-Advanced), on radio resource management, network coding and cooperative communications. Afif Osseiran is listed in Who’s Who in the World and Who’s Who in Science and Engineering. He has published more than 40 technical papers in international journals and conferences; and has hitherto more than 15 approved or pending patents. During his career, he has served as technical programme committee member, session chair, and reviewer in various international conferences, as well as a reviewer for international journals. Since 2006, he has been teaching, at Master’s level, a few lectures every year on Advanced Antennas at the Royal Institute of Technology (KTH) in Stockholm.

Preface This book summarises the results of collaborative research by partners from the manufacturing, network operator, research centre and university domains that was carried out in the Wireless World Initiative New Radio (WINNER) Project in Framework Programme 6 of the European Commission. The main objective of the WINNER project was the development of a radio interface proposal that fulfils the requirements of Systems Beyond 3G and IMT-Advanced as preparation for the forthcoming international standardisation process. This was the major project in Framework Programme 6 dealing with the radio interface development for IMTAdvanced in an international consortium. Different regions of the world use different names for this research area. In Europe ‘Systems beyond 3G’ was preferred; in Asia, and in particular in Japan, the name ‘fourth generation (4G)’ was introduced. The International Telecommunication Union – Radio Sector (ITU-R) finally agreed the name ‘IMT-Advanced’ globally to show the relation of further developments towards future systems based on third-generation mobile communication systems (IMT-2000). The focus of this book is the physical and medium access control layers of the WINNER radio interface proposal and all its elements. It provides the basic concepts and the architecture of a radio interface proposal, which can be used as input for the development of the IMT-Advanced radio interface for the forthcoming international standardisation. The system development was based on generic requirements from ITU-R and has taken into account global research activities in this domain. Chapter 1 provides an introduction to the subject. Chapter 2 introduces usage scenarios and technical requirements. Channel models are presented in Chapter 3. Chapter 4 explains the system concept and architecture. Modulation and coding techniques are presented in Chapter 5. Chapter 6 discusses link level procedures. The advanced antenna concepts for IMTAdvanced are described in Chapter 7. Chapter 8 describes the Layer-2 relays, newly introduced by the WINNER project to IMT-Advanced cellular networks. Multiple access schemes and inter-cell interference mitigation techniques are presented in Chapter 9. Chapter 10 summarises the radio resource control and system level functions. Chapter 11 discusses sharing and flexible spectrum use capabilities. The ITU-R spectrum requirements calculation methodology for IMT-Advanced is presented in Chapter 12. Chapter 13 describes the system model, test scenarios, and performance evaluation results. Finally, Chapter 14 presents a cost assessment and optimisation method for WINNER deployments. The WINNER project started in a first phase from January 2004 to December 2005 (WINNER I) and continued in a second phase, WINNER II, from January 2006 to December 2007. At project start, Recommendation M.1645 [ITU03] had just been approved by

xxvi

Preface

ITU-R and was taken as the baseline for the project. In the first project phase, a number of alternative concepts were investigated in order to identify the most promising building blocks for the overall system concept. These building blocks and their combination were investigated, further developed, optimised and validated in the second phase. This phased approach was a key to building consensus in an international consortium with partners from the countries and organisations listed in Tables 1 to 4. The WINNER project participated actively in international conferences and workshops to disseminate its research results and concepts as well as in international workshops. Public workshops were organised, in cooperation with other EU Framework Programme 6 projects from the Wireless World Initiative (WWI), in December 2004 in Brussels, Belgium, in October 2005 in Shanghai, China (in conjunction with the International Conference on Systems beyond 3G), in December 2005 in Paris, France, in November 2006 in Helsinki, Finland and in November 2007 in Brussels. The final project workshop in Munich, Germany in December 2007 presented the project results to representatives from international standardisation bodies, taking into account the outcome of the World Radiocommunication Conference (WRC) 2007. In addition, the project organised public demonstrations of a trial system at the Wireless World Research Forum (WWRF) meeting in Espoo, Finland in June 2007, the IST Mobile Summit in Budapest, Hungary in July 2007, the WRC 2007 in Geneva, Switzerland in October and November 2007, the public WWI Innovation Day in Brussels in November 2007 and the final project workshop in Munich in December 2007. The international composition of the WINNER consortium with major partners from the different stakeholders in mobile communications, namely manufacturers, network operators, research centres and academia, enabled consensus building on the basic system approach and contributions to the global regulatory process in ITU-R towards the preparation of WRC 2007.

Table 1

Countries involved in WINNER phases 1 and 2.

Country Austria Canada China Denmark Finland France Germany Greece Italy Poland Portugal Slovakia Spain Sweden Switzerland United Kingdom United States of America

WINNER I (2004 to 2005)

WINNER II (2006 to 2007)

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes

Preface

xxvii

Table 2 Manufacturers involved in WINNER phases 1 and 2. Manufacturers

WINNER I (2004 to 2005)

Alcatel SEL AG, Germany Alcatel-Lucent Deutschland AG, Germany Alcatel-Lucent Telecom Limited, UK BenQ Mobile GmbH & Co. OHG, Germany Elektrobit Corporation, Finland Elektrobit Ltd, Finland Elektrobit Testing Ltd, Finland Ericsson AB, Sweden Ericsson GmbH, Germany Fujitsu Laboratories of Europe Ltd, UK International Business Machines Corporation, Research, USA IBM Research GmbH, Zurich, Switzerland Lucent Technologies Network Systems UK Ltd, UK Motorola S.A.S., France Nokia Corporation, Finland Nokia (China) Investment Co., Ltd, Beijing, China Nokia Siemens Networks GmbH & Co. KG, Germany Nokia Siemens Networks S.p.A., Italy Nokia Siemens Networks Technology (Beijing) Co. Ltd, China Nortel Networks UK Ltd, United Kingdom Philips Electronics UK Ltd, UK QUALCOMM CDMA Technologies GmbH, Germany Samsung Electronics UK Ltd, UK Siemens AG, Germany ¨ Siemens AG Osterreich, Austria Siemens Ltd China, China Siemens Program and System Engineering SRO, Slovakia Siemens S.p.A., Italy

Table 3


Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes Yes Yes

Yes

Network operators involved in WINNER phases 1 and 2.

Operators DoCoMo Communications Laboratories Europe GmbH, Germany European Institute for Research and Strategic Studies in Telecommunications GmbH, Germany France Télécom S.A., France Portugal Telecom Inovaça˜ o S.A., Portugal Telefónica Investigación y Desarrollo Sociedad Anónima Unipersonal, Spain Vodafone Group Services Ltd, UK

WINNER I (2004 to 2005)


Yes Yes

Yes Yes

Yes Yes Yes

Yes Yes Yes

Yes

Yes

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Table 4

Preface

Research centres and academic institutions involved in WINNER phases 1 and 2.

Research centres and academic institutions Aalborg University, Denmark Carleton University, Canada Commissariat à l’Énergie Atomique (CEA-Léti), France Centre Technològic de Telecomunicacions de Catalunya, Spain Chalmers University of Technology, Sweden China Academy of Telecommunication Research, China Deutsches Zentrum für Luft- und Raumfahrt e.V., Germany Helsinki University of Technology, Finland Kungliga Tekniska Högskolan (KTH), Sweden National Technical University of Athens, Greece Poznan University of Technology, Poland Rheinisch-Westfälische Technische Hochschule Aachen (ComNets), Germany Swiss Federal Institute of Technology Zurich, Switzerland Technical Research Centre of Finland VTT, Finland Technische Universität Dresden, Germany Technische Universität Ilmenau, Germany Centre for Wireless Communications (CWC), University of Oulu, Finland The University of Surrey, UK

WINNER I (2004 to 2005)


Yes Yes

Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes Yes Yes Yes

Yes

Yes

In particular, the project contributed significantly to the ITU-R spectrum requirements calculation methodology for IMT-Advanced (Chapter 12) and to channel models (Chapter 3) for the evaluation of systems in the ITU-R, 3GPP and IEEE standardisation bodies. The WINNER project influenced indirectly, via member organisations of 3GPP, the Long Term Evolution (LTE) standardisation process starting with the 3GPP LTE workshop in November 2004 in Toronto, Canada. With respect to these activities, the WINNER project was able to impact the ongoing and forthcoming regulatory and standardisation process for IMT-Advanced. This successful work and achievements were possible due to the close cooperation of project partners and respect for the interests of the different organisations. It was a very good experience to cooperate in such an environment, where issues were discussed in an atmosphere of trust, to develop consensus and to resolve complex technical issues. This book aims to support the forthcoming detailed standardisation process for IMTAdvanced and to inform researchers and developers working towards specifying IMTAdvanced about the results gained in the WINNER project. Werner Mohr

Reference [ITU03] ITU-R, Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000, Recommendation M.1645, 2003.

Acknowledgements The material in this book originates from the EU Framework Programme 6 WINNER project and contributions to the international regulatory and standardisation process. Therefore, we would like to thank all colleagues involved in the project for their support and the good cooperation that made success possible. In addition to the successful completion of the project, this book was supported as an additional item which was not originally planned in the consortium. We hope that our personal relationships remain; a major part of the consortium is continuing its cooperation in a follow-up project. The WINNER project was partly funded by the European Commission in a Phase I from 2004 to 2005 and a Phase II from 2006 to 2007. We thank especially Dr Joao Schwarz Dasilva, Dr Rainer Zimmermann, Dr Francisco Medeiros and Dr Peter Stuckmann from the EU Commission for their continuous support. We would like to acknowledge the contributions of our colleagues from Aalborg University, Alcatel-Lucent Deutschland AG (formerly Alcatel SEL AG), Alcatel-Lucent Telecom Ltd (formerly Lucent Technologies Network Systems UK Ltd), BenQ Mobile GmbH & Co. OHG, DoCoMo Communications Laboratories Europe GmbH, Carleton University, Centre Technològic de Telecomunicacions de Catalunya, Chalmers University of Technology, China Academy of Telecommunication Research, Commissariat a` ´ l’Energie Atomique (CEA-Léti), Deutsches Zentrum für Luft- und Raumfahrt e.V., Elektrobit Corporation (formerly Elektrobit Ltd and Elektrobit Testing Ltd), Ericsson AB, Ericsson GmbH, European Institute for Research and Strategic Studies in Telecommunications GmbH, France Télécom S.A., Fujitsu Laboratories of Europe Ltd, Helsinki University of Technology, International Business Machines Corporation (formerly IBM Research GmbH Zurich), Kungliga Tekniska Högskolan (Royal Institute of Technology, KTH), Motorola S.A.S., National Technical University of Athens, Nokia Corporation, Nokia Siemens Networks GmbH & Co. KG (formerly Siemens AG), Nokia Siemens Networks S.p.A. (formerly Siemens S.p.A.), Nokia Siemens Networks Technology (Beijing) Co., Ltd (formerly Nokia (China) Investment Co., Ltd, Beijing), Nortel Networks UK Ltd, Philips Electronics UK Ltd, Portugal Telecom Inovaça˜ o S.A., Poznan University of Technology, QUALCOMM CDMA Technologies GmbH, Rheinisch-Westfälische Technische Hochschule Aachen, Samsung Electronics ¨ UK Ltd, Siemens AG Osterreich, Siemens Ltd China, Siemens Program and System Engineering SRO, Swiss Federal Institute of Technology Zurich, Technical Research Centre of Finland VTT, Technische Universität Dresden, Technische Universität Ilmenau, Telefónica Investigación y Desarrollo Sociedad Anónima Unipersonal, CWC University of Oulu, The University of Surrey, and Vodafone Group Services Ltd for continuous support of the project and for finalising this book.

xxx

Acknowledgements

We also would like to thank Mark Hammond, Sarah Tilley and Katharine Unwin from John Wiley & Sons for their help to finalise this book. Martin Döttling Werner Mohr Afif Osseiran

Abbreviations 1D 2D 2D-CP 2G 3G 3GPP 3GPP2 AAA ABR AC ACK ACS ACM ADC ADSL AES AF AGC AM AMC AMPS AN AoA AoD AP APP ARIB ARQ AS AS AT AuC AWGN B3G

One-Dimensional Two-Dimensional Two-Dimensional Cyclic Prefix Second Generation Third Generation Third Generation Partnership Project Third Generation Partnership Project No. 2 Authentication Authorisation Accounting Available Bit Rate Admission Control Acknowledge Admission Control Server Adaptive Coding and Modulation Analog-Digital Converter Asymmetric Digital Subscriber Line Advanced Encryption Standard (Ciphering Algorithm) Amplify and Forward Automatic Gain Control Acknowledged Mode Adaptive Modulation and Coding Advanced Mobile Telephone System Ambient Networks (Integrated Project) Angle Of Arrival Angle of Departure Access Point A Posteriori Association of Radio Industry and Business Automatic Repeat Request Angular Spread Access Stratum Allocation Table Authentication Center Additive White Gaussian Noise Beyond Third Generation

xxxii

BCCH BCH BCQI B-EFDMA BER BICM B-IFDMA BLA BLER BP BS BSFDD BSTDD BWA C/I CAPEX CBR CbS CC C-CDD CCDF CDD CDF CDL CDMA CE CEPT CF CG CFBS CJK CM CMR CNU COST CP CPA CPB CPC CPE CPG CPM CQI CR CRC CSI

Abbreviations

Broadcast Control Channel Broadcast Channel Best Channel Quality Indicator Block Equidistant Frequency Division Multiple Access Bit Error Rate Bit Interleaved Coded Modulation Block Interleaved Frequency Division Multiple Access Basic Link Adaptation Block Error Rate Belief Propagation Base Station Base Station using FDD mode Base Station using TDD mode Broadband Wireless Access Carrier-to-Interference Ratio Capital Expenditure Constant Bit Rate Connection-based Scheduling Convolutional Codes Cellular Cyclic Delay Diversity Complementary Cumulative Distribution Function Cyclic Delay Diversity Cumulative Distribution Function Clustered Delay Line Code Division Multiple Access Channel Estimation Conference of European Post and Telecommunications Cost Function Cost Group Cost Function Based Scheduling China, Japan, Korea (Initiative) Coded Modulation Coding and Modulation Rate Check Node Unit European Cooperation in the Field of Scientific and Technical Research Cyclic Prefix Common Pilots per Antenna Common Pilots per Beam Common Pilots per Cell Common Phase Error Conference Preparatory Group Conference Preparatory Meeting Channel Quality Indicator Cooperative Relaying Cyclic Redundancy Check Channel State Information

Abbreviations

CSIT CT CTF CWER DARPA DAS DBTC DCA DCT DET DF DFICE DFT DL DoA DPA DPA-FB DPB DPB-FB DPC DPIIC DRS DRX DS DSL E2E E2R EC ECC ECC PT1 ECSI EDGE EFDMA EIRP EKF EMF ES ES ETRI EU EVM EXIT FB FCA FCC FCFS

xxxiii

Channel State Information at Transmitter Configuration Table Channel Transfer Function Code Word Error Rate Defence Advanced Research Project Agency Distributed Antenna System Duo-Binary Turbo Codes Dynamic Channel Allocation Discrete Cosine Transform Dominant Eigenmode Transmission Decode and Forward Decision feedback Iterative Channel Estimation Discrete Fourier Transform Downlink Direction of Arrival Dedicated Pilots per Antenna Dedicated Pilots per Antenna over the Full Band Dedicated Pilots per Beam Dedicated Pilots per Beam over the Full Band Dirty Paper Coding Direct Parallel Inter-Cell Interference Cancellation Dynamic Resource Sharing Discontinuous Reception Delay Spread Digital Subscriber Line End-to-End End-to-End Reconfigurability European Commission Electronic Communication Committee Electronic Communication Committee Project Team 1 Equivalent Channel State Information Enhanced Data Rates for GSM Evolution Equidistant Frequency Division Multiple Access Equivalent Isotropically Radiated Power Extended Kalman Filter Electro-Magnetic Field Elevation Spread Earth Station Electronics and Telecommunications Research Institute European Union Error Vector Magnitude Extrinsic Information Transfer (Chart) Fixed Beam Fixed Channel Allocation Frame Control Channel First-Come First-Served

xxxiv

FCS FD FD-CTF FDD FDE FDMA FEC FER FFR FFT FH-LFDMA FI FIR FL FMIP FRN FSS FSU FTP FTTx GA GF GGSN GMC GoB GoS GPRS GPS GSM GW HARQ HIS HLR HMIP HO HOS HPA HRRM HSDPA HSPA HSS HSUPA HT HTTP HW HwC

Abbreviations

Far Cluster Scatterers Frequency Domain Frequency Domain Channel Transfer Function Frequency Division Duplex Frequency Domain Equalisation Frequency Division Multiple Access Forward Error Correction Frame Error Rate Fractional Frequency Re-use Fast Fourier Transform Frequency Hopping Localised Frequency Division Multiple Access Full-power Isolation Finite Impulse Response Floor Loss Fast Mobile IP Fixed Relay Node Fixed Satellite Service Flexible Spectrum Use File Transfer Protocol Fibre to the (Building/Curb/Home) Genetic Algorithm Galois Field Gateway GPRS Support Node Generalised Multi-Carrier Grid-of-Beams Grade of Service General Packet Radio Service Global Positioning System Global System for Mobile Communication Gateway Hybrid Automatic Repeat Request Hybrid Information System Home Location Register Hierarchy Mobile IP Handover Higher Order Sectorisation High-Power Amplifier Hybrid RRM High Speed Downlink Packet Access High Speed Packet Access Home Subscriber Server High Speed Uplink Packet Access Hilly Terrain Hypertext Transfer Protocol Hardware Horizontal Sharing with Coordination

Abbreviations

HwoC Hz IBDFE IBI IBSC IC ICE ICI ICNIRP ICU ID IdPIIC IEEE IF IFDMA IFFT IIC IMT IP IPA IPCL IQ IR IRC ISARP ISD ISHO ISI IT ITS ITU ITU-R ITU-R SG 5

xxxv

Horizontal Sharing without Coordination Hertz Iterated Block Decision Feedback Equalisation Inter-Block Interference Inter-Base-Station Communication Interference Cancellation Iterative Channel Estimation Inter-Cell Interference International Commission on Non-Ionising Radio Protection In-Cell user Identifier Indirect Parallel Inter-Cell Interference Cancellation Institute of Electrical and Electronics Engineers Intermediate Frequency Interleaved Frequency Division Multiple Access Inverse Fast Fourier Transform Inter-Cell Interference Cancellation International Mobile Telecommunications Internet Protocol IP Anchor Internet Protocol convergence layer In-phase/Quadrature Incremental Redundancy Interference Rejection Combining Interference-Based Self-Adaptive Re-use Partitioning Inter-Site Distance Inter-System Handover Inter-Symbol Interference Information Technology Intelligent Traffic System International Telecommunication Union International Telecommunication Union Radiocommunication Sector International Telecommunication Union Radiocommunication Sector Study Group 5 ITU-R SG 8 International Telecommunication Union Radiocommunication Sector Study Group 8 ITU-R WP 5D International Telecommunication Union Radiocommunication Sector Working Party 5D ITU-R WP 8F International Telecommunication Union Radiocommunication Sector Working Party 8F Iu-Flex 3GPP Interface KPI Key Parameters Indicators L2 Layer 2 LA Local Area LA Link Adaptation LAN Local Area Network

xxxvi

LB LBCCH LCCCH LDC LDCCH LDPC LDPCC LDTCH LFDMA LFSR LI LLR LMCCH LMMSE LMS LMTCH LN LOS LPCH LPIF LS LS LSP LT LTE MA MAC MADM MAI MAP MBMS MBS MCBC MC-CDMA MCN MCS MCSSS MI-ACM MIDCA MIESM MIMO MIP MISO mITF MM MMR

Abbreviations

Logical Beam Logical Broadcast Control Channel Logical Common Control Channel Linear Dispersion Code Logical Dedicated Control Channel Low Density Parity Check Low Density Parity Check Code Logical Dedicated Traffic Channel Localised Frequency Division Multiple Access Linear Feedback Shift Register Length Indicator Log-Likelihood Ratio Logical Multicast Control Channel Linear Minimum Mean Square Error Least Mean Square Logical Multicast Traffic Channel Logical Node Line Of Sight Logical Paging Control Channel Lowpass Interpolation Filter Large Scale Least Squares Large-Scale Parameter Long-Term Long Term Evolution Metropolitan Area Medium Access Control Multiple Attribute Decision Making Multiple Access Interference Maximum A Posteriori Multicast Broadcast Messaging Service Multi-Band Scheduler Multicast/Broadcast Multi-Carrier Code Division Multiple Access Multi-hop Cellular Network Modulation and Coding Scheme Multi-Carrier Spread Spectrum Signal Mutual Information based Adaptive Coding and Modulation Minimum Interference Dynamic Channel Allocation Mutual Information Effective SINR Metric Multiple Input Multiple Output Mobile IP Multiple Input Single Output Mobile IT Forum Mobility Module Mobile Multi-hop Relaying

Abbreviations

MMS MMSE MPC MPEG MPIIC m-PSK m-QAM MRC MS MSA MSE MU MUD MU-MIMO MUX MVNO NACK NAS NGMC NGMN NIP NLOS NSF O&M OCI ODMA ODS OF OFDM OFDMA OPEX OS OSI OTA OVSF P2P PACE PADC PAN PAPC PAPR PARC PAS PBCH PDA PDC

xxxvii

Multimedia Messaging Service Minimum Mean Square Error Multipath Component Moving Picture Experts Group Mean Parallel Inter-Cell Interference Cancellation Phase-Shift Keying with m constellation points Quadrature Amplitude Modulation with m constellation points Maximum Ratio Combining Mobile Station Min-Sum Algorithm Mean Square Error Multi-User Multi-User Detector Multi-User Multiple Input Multiple Output Multiplexing Mobile Virtual Network Operator Negative Acknowledgement Non-Access Stratum Next Generation Mobile Committee Next Generation Mobile Networks Noise-plus-Interference Power Non Line Of Sight National Science Foundation Operation and Maintenance Out-of-Cell Interferer/Interference Opportunity Driven Multiple Access Optimum Distance Spectrum Objective Function Orthogonal Frequency Division Multiplexing Orthogonal Frequency Division Multiple Access Operational Expenditure Objective Score Open System Interconnection Over-the-Air Orthogonal Variable Spreading Factor Peer-to-Peer Pilot Assisted Channel Estimation Physical Frequency-Adaptive Data Channel Personal Area Network Per Antenna Power Constraint Peak-to-Average Power Ratio Per Antenna Rate Control Power Azimuth Spectrum Physical Broadcast Channel Personal Digital Assistant Personal Digital Cellular System

xxxviii

PDCFC PDCP PDF PDFCC PDP PDU PER PF PHY PI PI PIC PID PIIC PLM PLO PM PMBC PN PNDC PPP PRACH PSK PSRC PSU PUCH QAM QC-BLDPC QoS QPSK R3SARP RA RACH RAN RAP RAT RAU RBD RCDD REC RF RLAN RLC RMS RN RNC

Abbreviations

Physical Downlink Control Format Indicator Channel Packet Data Convergence Protocol Probability Density Function Physical Downlink Frame Control Channel Power Delay Profile Protocol Data Unit Packet Error Rate Proportional Fair Physical Layer Paging Indication Partial Power Isolation Parallel Interference Cancellation Packet Identifier Parallel Inter-Cell Interference Cancellation Physical Layer Mode Phase Locked Oscillator Paging Message Physical Multicast Broadcast Channel Pseudo-Noise Physical Non-Frequency-Adaptive Data Channel Point-to-Point Protocol Physical Random Access Channel Phase-Shift Keying Per Stream Rate Control Percentage of Satisfied Users Physical Uplink Control Channel Quadrature Amplitude Modulation Quasi-Cyclic Block Low Density Parity Check (Code) Quality of Service Quaternary Phase-Shift Keying (4-QAM) Re-use 3 Self-Adaptive Re-use Partitioning Rural Area Random Access Channel Radio Access Network Radio Access Point Radio Access Technology Resource Allocation Unit Regularised Block Diagonalisation Relay Cyclic Delay Diversity Relay-Enhanced Cell Radio Frequency Radio Local Area Network Radio Link Control Root Mean Square Relay Node Radio Network Controller

Abbreviations

RP RPC RR RRC RRM RS RSARP RT RTP RTT RTTM RTU RU RVQ RX S1-Flex SAE SAP SARP SC SCM(E) SCN SDH SDMA SDO SDU SE SF SF SFN SFR SGSN SI SIC SIMO SINR SIR SISO SLA SMMSE SMS SMUX SN SNDR SNR S-PARC

xxxix

Resource Partitioning Radio Paging Controller Round Robin Radio Resource Control Radio Resource Management Resource Scheduler Random Self-Adaptive Re-use Partitioning Real Time Real Time Protocol Round Trip Time Real Time Traffic Measurement Retransmission Unit Resource Unit Random Vector Quantisation Receiver 3GPP interface System Architecture Evolution Service Access Point Self-Adaptive Re-use Partitioning Service Category; Study Case (Ch. 14) Spatial Channel Model (Wideband Extension) Single-hop Cellular Network Synchronous Digital Hierarchy Space Division Multiple Access Standards Development Organisation Service Data Unit Service Environment Shadow Fading Super-Frame Single Frequency Network Soft Frequency Re-use Serving GPRS Support Node Self Interference Successive Interference Cancellation Single Input Multiple Output Signal-to-Interference-plus-Noise Ratio Signal-to-Interference Ratio Single Input Single Output Service Level Agreement Successive Minimum Mean Square Error Short Message Service Spatial Multiplexing Sequence Number Signal-to-Nonlinear-Distortion Ratio Signal-to-Noise Ratio Selective Per Antenna Rate Control

xl

SPICE SR-ARQ SRC SSC SS-MC-MA ST STBC STC SU SUC SVD TA TB TBCH TCM TCP TDD TDM TDMA TFT TLSP TM TMCH TMSI TP TPCH TRAC TSCH TTI TU TX U(P) UBR UDP UE UHF UIA2 UL ULA UM UMTS UN UT UTRAN VBR VNU

Abbreviations

Service Platform for Innovative Communication Environment Selective Repeat-ARQ Spectrum Resource Change Spectrum Sharing and Coexistence Spread Spectrum Multi-Carrier Multiple Access Short-Term Space Time Block Code Space Time Coding Single User Satisfied User Criterion Singular Value Decomposition Tracking Area Transmission Block Transport Broadcast Channel Trellis Coded Modulation Transmission Control Protocol Time Division Duplex Time Division Multiplex Time Division Multiple Access Transport Format Table Transformed LSP Transparent Mode Transport Multicast Channel Temporal Mobile Subscriber Identity Throughput Transport Paging Channel Transport Random Access Channel Shared Transport Channel Transmission Time Interval Typical Urban Transmitter User (Plane) Unspecified Bit rate User Datagram Protocol User Equipment Ultra High Frequency Ciphering algorithm Uplink Uniform Linear Array Unacknowledged Mode Universal Mobile Telecommunications System United Nations User Terminal Universal Terrestrial Radio Access Network Variable Bit Rate Variable Node Unit

Abbreviations

VoIP VS VSF-OFCDM WA WARC WG SERV WG SPEC WG TECH WIF WiMAX WINNER WLAN WPAN WRAN WRC WSI WWI WWL WWRF

xli

Voice Over Internet Protocol Vertical Sharing Variable Spreading Factor Orthogonal Frequency and Code Division Multiplexing Wide Area World Administrative Radio Conference Working Group Future Services and Market Aspects Working Group Spectrum Working Group Technology Wiener Interpolation Filter Worldwide Interoperability for Microwave Access Wireless World Initiative New Radio Wireless Local Area Network Wireless Personal Area Networks WINNER Radio Access Network World Radiocommunication Conference Wireless Strategic Initiative Wireless World Initiative Wireless Local Loop Wireless World Research Forum

List of Contributors Dr. Saied Abedi, Wireless Technology, Networks Systems Research Division, Fujitsu Laboratories of Europe LTD. (FLE), Hayes Park Central, Hayes End Road, Hayes, Middlesex, UB4 8FE UK Dr. Gunther Auer, DOCOMO Communications Laboratories Europe GmbH, Landsberger Str. 312, D-80687 Munich, Germany Mr. Mehdi Bennis, University of Oulu, Centre for Wireless Communications (CWC), PO Box 4500, Oulu, Finland Dr. Ivan Cosovic, formerly with DOCOMO Communications Laboratories Europe GmbH, Landsberger Str. 312, D-80687 Munich, Germany Mr. Klaus Doppler, Nokia Research Center, P.O. Box 100, 00045 NOKIA GROUP, Finland Dr. Martin Döttling, Nokia Siemens Networks GmbH & Co. KG, St.-Martin-Str. 76, D-81541 Munich, Germany, now with European Patent Office, Germany Prof. David Falconer, Carleton University, Ottawa, Canada Dr. Roberta Fracchia, formerly with Motorola Labs - Paris, Parc Les Algorithmes - Saint Aubin, 91193 Gif sur Yvette - France Mr. Lassi Hentilä, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Mr. Joerg Huschke, Ericsson Research, Corporate Unit, Ericsson GmbH, Eurolab R&D, Ericsson Allee 1, D-52134 Herzogenrath, Germany Dr. Ralf Irmer, Principal Engineer, Vodafone Group Research & Development, Newbury, UK Dr. Tim Irnich, Ericsson GmbH, Kackertstrasse 7-9, D-52072 Aachen, Germany, formerly with Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany Mr. Jean-Philippe Javaudin, Access Networks, Orange Labs, France Telecom, 4 rue du Clos Courtel, 35512 Cesson-Sévigné, France Mr. Paulo Jesus, Portugal Telecom Inovaça˜ o, S.A., R. Eng. José Ferreira Pinto Basto, 3810 106 Aveiro, Portugal

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Mr. Niklas Johanson, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Mr. Tommi Jämsä, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Mr. Kari Kalliojarvi, Nokia Oyj, Visiokatu 1, FI-33720 Tampere, Finland Mr. Jörn von Häfen, R&S BICK Mobilfunk GmbH, Fritz-Hahne-Str. 7, D-31848 Bad Münder, Dept. E, Germany, formerly with Nokia Siemens Networks GmbH & Co. KG, St. Martin Strasse 76, D-81541 Munich, Germany Mr. Pekka Kyösti, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Dr. Thierry Lestable, formerly with Samsung Electronics UK Ltd, UK, no with Sagem Communications, France Dr. Jijun Luo, Nokia Siemens Networks GmbH & Co. KG, St.-Martin-Strasse 76, D-81541 Munich, Germany Ms. Geneviève Mange, Alcatel-Lucent Germany, Bell Labs, Lorenzstr. 10, D-70435 Stuttgart Ms. Marja Matinmikko, VTT Technical Research Centre of Finland, Kaitoväylä 1, FI-90571 Oulu, Finland Mr. Juha Meinilä, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Ms. Albena Mihovska, Aalborg University, Niels Jernes Vej, 12, 9220 Aalborg, Denmark Mr. Emilio Mino Diaz, Telefonica Investigación y Desarrollo, Emilio Vargas, 6, 28043 Madrid, Spain Mr. Peter Moberg, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Dr. Werner Mohr, Nokia Siemens Networks GmbH & Co. KG, St. Martin Strasse 76, D-81541 Munich, Germany Dr. Johan Nyström, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Mr. Pekka Ojanen, Nokia Corporation, P.O. Box 100, 00045 NOKIA Group, Finland Mr. Jussi Ojala, Nokia Corporation, P.O. Box 407, 00045 NOKIA Group, Finland Dr. Afif Osseiran, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Mr. Ralf Pabst, Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany Dr. Stephan Pfletschinger, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Parc Mediterrani de la Tecnologia, Av. del Canal Ol´ımpic s/n, 08860 Castelldefels, Spain Dr. Simone Redana, Nokia Siemens Networks GmbH & Co. KG, St. Martin Strasse 76, D-81541 Munich, Germany


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Dr. Stéphanie Rouquette-Léveil, Motorola S.A.S., Seamless Radio Access Lab, Parc les Algorithmes, Saint-Aubin, 91193 Gif sur Yvette Cedex, France Mr. Daniel Chr. Schultz, formerly with Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany, now with Detecon, Germany Prof. Mikael Sternad, Signals and Systems, Uppsala University, P O Box 534, SE-751 21 Uppsala, Sweden Dr. Tommy Svensson, Communication Systems Group, Department of Signals and Systems, Chalmers University of Technology, SE-412 96 Göteborg, Sweden Dr. Shyamalie Thilakawardana, CCSR, The University of Surrey, Guildford, Surrey, GU2 7XH, UK Mr. Elias Tragos, Athens University, Heroon Polytechniou 9, 15780 Zografou, Athens, Greece Prof. Dr. Bernhard H. Walke, Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany Dr. Marc Werner, QUALCOMM CDMA Technologies GmbH, Nordostpark 89, D-90411 Nuremberg, Germany Dr. Carl Wijting, Nokia Research Center/Wireless Systems and Services, P.O. Box 407, FI-00045 NOKIA Group, (Itämerenkatu 11-13, 00180 Helsinki, Finland) Ms. Roufia Yahi, Orange Labs, Spectrum Management Engineer, 38-40 rue du Général Leclerc, 92794 Issy-Moulineaux cedex 9, France

1 Introduction Werner Mohr Nokia Siemens Networks

This chapter describes the global development and status of mobile and wireless communications from the deployment of analogue first-generation systems towards the current global research activities on systems beyond 3G/IMT-Advanced. Development towards digital mobile communication systems in the second generation enabled successful, global mass-market penetration. Third-generation (3G) systems are providing improved user experience for data applications. In parallel, the wireless IT sector is developing systems for short-range and wide-area applications. Based on these developments and expected future traffic growth, requirements and basic system concepts for IMT-Advanced have been developed by ITU-R and major network operators. Research activities on such systems started about 1999 in all regions of the world. The European WINNER project is one major effort for the development of an IMT-Advanced radio interface concept under Framework Programme 6 of the European Commission. All these developments supported the identification of additional frequency spectrum for mobile and wireless communications in the World Radiocommunication Conference (WRC) 2007. After WRC 2007, the system concept has to be adapted to its outcome. In parallel with the forthcoming standardisation process, the necessary research activities for adapting and optimising the system concept are being continued in the European Eureka Celtic project WINNER+ as a follow-up to the WINNER project.

1.1 Development and Status of Mobile and Wireless Communications Mobile and wireless communication systems have been successfully deployed in different regions of the world since the 1980s. In the first generation, differing analogue systems were deployed mainly in the developed regions of the world to support telephony services for mobile subscribers. In Europe, different systems were deployed in different countries, which did not allow roaming between countries. In the Americas, a single Advanced Mobile Telephone Radio Technologies and Concepts for IMT-Advanced Martin Döttling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

2

Radio Technologies and Concepts for IMT-Advanced

System (AMPS) was deployed. Japan developed its own system as a derivative of AMPS [Wal02; S91; Lee90]. With respect to small national markets for different analogue systems in Europe, the Conférence Européenne des Administrations des Postes et des Télécommunications (CEPT) decided in 1982 to develop a pan-European second-generation mobile communication system. This was the starting point of the Global System for Mobile Communication (GSM), which was deployed internationally from 1991. In the beginning, the main objective of GSM was the support of voice telephony and international roaming with a single system across Europe. GSM is based on time division multiple access (TDMA) and digital signal processing, which is the main technical advance over first-generation systems [Hil02]. In parallel with GSM, other digital second-generation systems were developed globally and competed with each other. The USA followed a market-driven approach with competing technologies for the same service. Japan deployed a national second-generation system, Personal Digital Cellular (PDC) System, which was not compatible with other second-generation systems. Today, the GSM family (GSM, General Packet Radio Service (GPRS) and Enhanced Data Rates for GSM Evolution (EDGE)) is the dominant second-generation mobile communication standard with a global market share (at the end of the second quarter of 2008) of more than 86 % and 2.975 billion subscribers in more than 200 countries [GSA08]. The main competitor to GSM is IS-95 CDMA, which is based on code division multiple access (CDMA) [Vit95]. The CDMA system family, including third-generation systems, supports 451 million subscribers globally [CDG08]. Currently, the main subscriber growth markets for the GSM system are emerging markets, such as China with about 7 million new subscribers per month and India with about 5 million new subscribers per month. At the end of 2007, more than 3 billion subscribers in total were connected to mobile communications globally. Mobile and wireless communications are serving user needs with increasing penetration rates on a global basis. Mobile communication systems enable many new applications and allow more flexibility for users and thereby an improvement of quality of life and efficiency of business processes. In parallel with the fast growth of second-generation mobile communication, third-generation systems were developed from about 1988 and deployed globally since 2002. The introduction of third-generation (3G) mobile communication systems, International Mobile Telecommunications (IMT-2000), provided a mobile broadband access system, which increases the opportunities for data applications and new business models [UMTS05]. The main representatives are Universal Mobile Telecommunications System (UMTS), based on the integration of this new radio access system on the GSM network infrastructure, and cdma2000, a further development of IS-95 CDMA. The first UMTS systems supported peak data rates of 384 kbps. UMTS and cdma2000 are being further developed for increased performance. High Speed Packet Access (HSPA), a UMTS variant, supports peak downlink data rates from 7.2 Mbps to about 14.6 Mbps (see for example [HT07]) by using adaptive modulation and coding with higher-order modulation and multicode transmission. This system was launched in 2007. At the end of the second quarter of 2008, UMTS and its further developments reached about 235.5 million subscribers globally [GSA08]. UMTS is being further developed by the 3rd Generation Partnership Project (3GPP) [3GPP] towards the Long Term Evolution (LTE) system which has peak data rates of more than 100 Mbps, increased spectral efficiency and significantly shorter latency than today’s systems. LTE is based on orthogonal frequency division multiple access (OFDMA) and advanced spatial processing – multiple input multiple output (MIMO). Similar activities are ongoing for the further development of the cdma2000 family [3GPP2].

Introduction

3

The Next Generation Mobile Networks (NGMN) initiative, which is supported by international network operators, has formulated requirements on further developments of mobile communications [NGMN]. Such requirements are mainly related to a flat network architecture based on the Internet protocol (IP) for cost reduction, higher spectral efficiency for better use of the available frequency spectrum, lower latency and higher peak data rates with flexible allocation of data rates to users. Additional requirements are a high cell average throughput and sufficiently high cell edge capacity in order to cover the expected increasing data traffic with growing user density. LTE is already being developed towards these requirements, which is an important step towards IMT-Advanced (see Section 1.2). In parallel with these developments in the telecommunications industry, the wireless IT sector provides different IP-based access systems for different application areas. Wireless LAN (WLAN) systems, in the standards family IEEE802.11, are used for local and shortrange applications without mobility. WLAN systems are widely available globally. Wireless Personal Area Networks (WPAN) are standardised by IEEE802.15 for very short ranges and high throughput. Broadband wireless access (BWA) systems, according to IEEE802.16, are looking for higher ranges including the support of user mobility [IEEEIO; IEEESG]. The BWA WiMAX system is a member of the IMT-2000 family [WIMAX].

1.2 Expectations of Data Traffic Growth The expected economic impact of mobile and wireless communications providing improved productivity in business processes and access to information any time and anywhere is driving the further improvement of communication systems. Traffic over mobile and wireless systems is expected to increase significantly especially for data applications [UMTS05]. Data traffic is strongly increasing mainly due to Internet traffic. In preparation for the World Radiocommunication Conference (WRC) 2007, the European Commission initiated a market study of mobile and wireless communications [FBB05], which predicted a significant increase in traffic in various scenarios, confirming the expectations of fora such as the Wireless World Research Forum (WWRF) and the industry. In addition, the International Telecommunication Union – Radio Sector (ITU-R) has the objective of significantly increasing global teledensity, which should connect about 5 billion people by 2015. Based on these studies, ITU-R developed a market report and spectrum demand estimates. The EU Framework Programme 6 Wireless World Initiative New Radio (WINNER) project contributed to this activity [TW08]. In developed regions and countries, data traffic is mainly carried by fixed networks and minor amounts are carried via mobile and wireless systems. Data traffic per user is increasing significantly. If a small part of the traffic moves from fixed networks to mobile and wireless systems, this results in a huge increase in traffic on mobile and wireless systems [Wal07; WWRF]. In emerging regions and countries, a dense and broadband fixed network is not yet available especially in less densely populated areas. Therefore, the deployment of mobile and wireless systems is a faster and more economic solution. These trends show the need for increased radio network capacity and throughput by improved systems and the identification of additional frequency spectrum. WRC 2007 identified additional frequency spectrum to reply to these needs (Chapter 12). However, the amount of frequency spectrum identified is less than that required by market studies and is different in different regions and countries.

4


1.3 Development Towards IMT-Advanced These increasing traffic expectations require the further development of future mobile and wireless systems. Research activities on IMT-Advanced started in different regions even before the launch of third-generation systems. Around 1999, initial research work started in Europe on systems beyond 3G and in Japan even earlier on 4G systems. In Europe, the Wireless Strategic Initiative (WSI) project in Framework Programme 5 of the EU developed a first concept of systems beyond 3G. This project launched the global Wireless World Research Forum (WWRF) [WWRF] in 2001 as a platform for building consensus to further develop the vision and basic concepts and algorithms and to identify the major building blocks for future systems [Taf05; Taf06; MK00]. Today’s systems (see Section 1.1) are developed independently for different application areas such as cellular-based mobile communication systems for nationwide coverage and the support of full mobility and international roaming; short-range communications (WLAN-type systems) for nomadic use and local coverage; broadband wireless access systems for Digital Subscriber Line (DSL), last-mile applications and broadcast systems. Users want personalised mobile multimedia services with any content, anywhere, at any time, via any device and any access system. The necessary system complexity has to be hidden from the user via easy-tohandle user interfaces. From the user perspective, the vision for mobile communications can be described as a multisphere level concept (see Figure 1.1), which was developed in the WSI project around 2001 and further detailed in an early edition of the WWRF ‘book of visions’ [WWRF; Moh03a]. This basic vision will remain in the future. In the world beyond 3G, the user-centric approach will enable people to communicate anywhere, any time to anybody and will improve today’s user experience by seamless access to mobile and wireless systems. ITU-R developed and approved, in 2003, Recommendation M.1645 on the future development of IMT-2000 (3G) and systems beyond it [ITU03], which was the basis for the preparation of WRC 2007. Future systems will comprise a network of networks of cooperating heterogeneous access systems via horizontal handover within the same access system and vertical handover between different access systems for seamless access (see Figure 1.2). The access systems will be integrated on

Figure 1.1 The multisphere level concept: IMT-Advanced will cover different communication relations. (Reproduced with kind permission of Springer Science and Business Media © 2008).

Introduction

5

Services and Applications New Radio Interface download channel

return channel e.g. cellular

Digital Broadcast

IP based IP-based Core Network

Cellular 2nd gen. gen.

IMT-2000

xDSL Wireline xDSL WLAN WLAN WLAN type type

other entities Short Range Short-Range Connectivity

Figure 1.2 ITU-R vision for IMT-Advanced. (Reproduced by Permission of IEEE © 2009).

an IP-based core network platform. This overall vision will integrate wide-area and short-range systems as well as public licensed and unlicensed systems, which complement each other. The scope of this book is the new radio interface in Figure 1.2. It should support the following basic generic capabilities:

r New mobile access: Peak aggregate useful data rate up to approximately 100 Mbps and a mobile speed up to 250 km/h.

r New nomadic/local area wireless access: Peak aggregate useful data rate up to approximately 1 Gbps. These targets are summarised in a ‘van-diagram’ in terms of data rate versus mobility (see Figure 1.3). The international specification bodies 3GPP and 3GPP2 are already extending the capabilities of 3G systems towards higher peak data rates and lower latency (e.g. in the UMTS family: High Speed Downlink Packet Access (HSDPA); High Speed Uplink Packet Access (HSUPA); and LTE), which corresponds to enhanced IMT-2000. Supporting such capabilities with sufficient average cell throughput and cell edge capacity will require wider carrier bandwidth than today’s systems [Moh03b]. Such wider bandwidth may only be available in frequency bands above the bands for current systems. WRC 2007 identified additional frequency spectrum in different bands including the UHF band below 1 GHz and in the C band around 3.5 GHz. At higher frequency bands and for significantly higher data rates than today’s systems, the covered range will be smaller [MLM02]. In order to provide economic coverage under these conditions, new deployment schemes such as relay-based or multihop systems are getting much attention in the research community. The additional frequency spectrum will most probably not be available exclusively for mobile and wireless communications and sharing scenarios will play an important role.

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Systems beyond IMT-2000 will encompass the capabilities of previous systems

Mobility

New capabilities of systems beyond IMT-2000

High

IMT-2000

Enhanced IMT-2000

New Mobile Access

Dashed line indicates that the exact data rates associated with systems beyond IMT-2000 are not yet determined.

Enhancement

New Nomadic / Local Area Wireless Access

Low

1

KEY:

10 1 00 Peak Useful Data Rate (Mb/s)

1 000

denotes interconnection between systems via networks, which allows flexible use in any environment without making users aware of constituent systems. Nomadic / Local Area Access Systems

Digital Broadcast Systems

Dark shading indicates existing capabilities; medium shading indicates enhancements to IMT-2000; and the lighter shading indicates new capabilities of systems beyond IMT-2000. Low mobility covers the speed pedestrian and high mobility covers speed highways or fast trains (60km/h to 250>km/h, or more).

Figure 1.3 The capabilities of IMT-2000 and systems beyond IMT-2000 [ITU03]. (Reproduced with kind permission of ITU © 2009).

1.4 Global Research Activities There are two trends of global research activities on future radio access systems [Moh08]. Research activities in Asian countries (China, Japan, Korea (CJK)) are mainly following the ITU-R Recommendation M.1645 [ITU03] with respect to Figures 1.2 and 1.3. In Europe and in telecommunication-oriented manufacturers in North America, research is based on requirements from ITU-R and bodies such as 3GPP. The global wireless IT sector is supporting application-specific radio access systems (see Section 1.1), e.g. in the context of IEEE standardisation [IEEEIO; IEEESG], which are directly connected to the Internet. Also the wireless IT sector is working towards ITU-R requirements in M.1645. The Chinese government has launched the research framework program 863. Future mobile communication systems are investigated in the FuTURE project. The concepts and results of this project are discussed with international partners in the FuTURE Forum [FF]. The system concept is based on distributed radio systems and antennas using radio over fibre in order to

Introduction

7

improve system capacity and coverage. The Chinese research community and industry are cooperating closely. In Japan, the activities are coordinated in the mobile IT Forum [mITF] and Advanced Wireless Communications Study Committee in the context of the Association of Radio Industry and Business [ARIB]. The concepts and requirements are summarised in reports [mITF03; mITF04; mITF05a; mITF05b]. The mITF reference model combines radio access as a heterogeneous environment of different systems, the packet-based core network, the service support platform and, on top of that, services and applications. The different radio access systems cooperate via the core network. This reference model is very similar to the ITU-R Recommendation M.1645 [ITU03]. Korea is following a similar approach to that of Japan. The Next Generation Mobile Committee [NGMC] is coordinating the national activities. Basic research activities are performed by the Electronics and Telecommunications Research Institute (ETRI) [ETRI]. There are different evolution and migration paths from Personal Area Networks (PANs), WLAN-type systems, wireless local loops (WWLs), Intelligent Traffic Systems (ITS), cellular and broadcast systems [Moh08]. In the different paths, the systems are evolving towards higher throughput values. The integration of these different systems results in the IMT-Advanced system similar to [ITU03]. In parallel, Korea is actively supporting the approach of the wireless IT sector based on IEEE802.16 as an intermediate step towards IMT-Advanced. China, Japan and Korea are cooperating at government level and between standards development organisations (SDOs) towards common objectives in the CJK initiative. In Europe, the European Commission addressed the area of systems beyond 3G in the Work Plan of Framework Programme 6, which is continuing in Framework Programme 7 [ECC]. Several complementary and cooperating projects have been launched in this domain (see Section 1.5). These European Commission Framework Programmes are complemented by related national programmes and projects in different Member States and Eureka Clusters at the European level [EUC], where nationally funded projects are cooperating. The global wireless IT sector is supporting systems for different radio access scenarios. The main activity is in the IEEE standardisation process [IEEEIO; IEEESG], where the following systems are currently under consideration:

r IEEE802.11 series for WLAN applications; r IEEE802.15 series for short-range communications; r IEEE802.16 series for fixed wireless access with a roadmap towards mobility; r IEEE802.20 series for cellular applications; r IEEE802.21 for interworking issues; r IEEE802.22 for wireless regional area networks. In the USA, significant effort is spent on software defined and cognitive radio systems for future applications and a different use of the frequency spectrum. The main research activities have been launched by the Defense Advanced Research Projects Agency (DARPA) in the neXt Generation (XG) communications program [DARPA] and the National Science Foundation (NSF) [NSF] mainly for the academic domain. In this global environment, the WINNER project in Framework Programme 6 of the European Commission was launched as an international consortium to develop a system concept including key algorithms, protocols and an architecture that support the ITU-R generic

8


requirements and ongoing developments in the global regulatory and standardisation domain [WIN].

1.5 WINNER Project The WINNER project was launched at the beginning of Framework Programme 6 of the European Commission. This project was organised in two phases, from January 2004 to December 2005 (WINNER I) and from January 2006 to December 2007 (WINNER II). This project was the response to the request from ITU-R to the research community to investigate the feasibility of generic requirements for IMT-Advanced. Major research challenges were derived from these requirements and other global research activities on IMT-Advanced (see Section 1.4). IMT-Advanced should provide significantly higher throughput than available systems. The related greater carrier bandwidth may only be available at higher carrier frequencies, which both reduces the available range and coverage [Moh03b; MLM02]. Therefore, finding economic methods of deployment for future systems was a major research challenge. With respect to the limited frequency bands, a high average cell spectral efficiency and cell edge capacity are important requirements. Link level procedures, advanced antenna concepts, resource management schemes, interference mitigation techniques, efficient protocols, and spectrum usage and sharing technologies are essential for efficient use of the available frequency spectrum. Only the best combination of these different technologies and their optimisation can ensure meeting the requirements. Therefore, system design and optimisation at the system level, taking into account the radio network in addition to the link level, is necessary. The overall objectives of the WINNER project can be summarised as follows [Moh07]:

r To develop a ubiquitous scalable radio access system based on common radio access technologies with enhanced capabilities, compared to existing systems and their evolutions, that will adapt to and be driven by user needs and a comprehensive range of short-range to wide-area mobile communication scenarios. This should be achieved by utilising advanced and flexible network topologies, physical layer technologies and frequency sharing methods, where the different scenarios are addressed by optimised parameter settings. r To base the design of the radio system on a horizontal integration for different radio environments and spectrum conditions in terms of frequency range and carrier bandwidth with respect to the availability of frequency spectrum. r To make efficient use of the radio spectrum in order to minimise the cost per bit. r To define the system in such a way that it can be realised through cost competitive infrastructure and terminals. All relevant areas of the radio interface have been investigated and concepts, algorithms and protocols have been developed. Major achievements of the project are in the following areas:

r the overall system concept and architecture including the multiple access system, modulation and coding, scheduling, medium access control (MAC), radio resource management (RRM) and radio link control (RLC);

Introduction

9

r the system architecture, which has a flat architecture, relay logical nodes, a spectrum server, cross-layer optimisation of protocols and a focus on scalability and flexibility;

r development and optimisation of coding schemes, such as LDPCC, DBTC and QC-BLDPC codes, and the link adaptation scheme for frequency-adaptive transmission and channel coding; r link level procedures such as channel estimation, link adaptation, a pilot channel scheme and synchronisation for SISO and MIMO systems; r interference averaging and mitigation techniques including random dynamic channel allocation; r deployment concepts, such as relaying for different radio environments including protocols, radio resource management and cost assessment; r cooperation (mobility management, congestion avoidance control and QoS-based management) between legacy systems and the WINNER concept; r the performance evaluation methodology for extensive system simulation, for optimisation and validation purposes; r channel models (path loss, wideband and MIMO) for system evaluation, contributed to international regulatory and standards bodies; r the spectrum requirements calculation tool, which was adopted by ITU-R for the preparation of WRC 2007 [TW08]; r extensive contributions to the global regulatory and standardisation process in ECC PT1, ITU-R, 3GPP and IEEE on spectrum requirements and spectrum sharing towards the preparation of WRC 2007, on channel modelling and indirectly on 3GPP LTE via project partners; r investigation of implementation issues and complexity. In addition, the project collected user requirements and traffic models. All building blocks are combined and optimised jointly to develop the overall system concept. These activities were carried out for wide-area, metropolitan-area and local-area scenarios. Finally, the project performed trials of key functions of the system concept. The WINNER project was part of a bigger research initiative, the Wireless World Initiative (WWI), where several projects in Framework Programme 6 were cooperating to investigate all major components of the value constellation of future mobile and wireless communications in the sense of end-to-end solutions from a system perspective [MA06]. In addition to the WINNER project, WWI comprised the following projects:

r Ambient Networks (AN): Development of a ‘seamless network’ for heterogeneous networks [NSZ07];

r End-to-End Reconfigurability (E2 R): Investigation of reconfigurability of networks [BE05]; r MobiLife: Looking at services and applications from the end user and terminal perspective [Kle07];

r Service Platform for Innovative Communication Environment (SPICE): Development of service platform concepts [MSK06].

1.6 Future Work The major activities in international standardisation (e.g. 3GPP and IEEE), the regulatory process in ITU-R, and WRC 2007 and its implementation are shown in Figure 1.4. International

10


HSDPA, start of deployment 3G evolution (LTE, NGMN) 3GPP - 3G evolution

HSUPA

IEEE802.11n / 16 / (WiMAX, WiBro in Korea) start of deployment IEEE and related activities Deployment Spectrum Implementation WRC 2007

Standardisation

Regulation (ITU-R Framework Recommendation) WINNER WINNER+ WINNER2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Figure 1.4 Timeline of activities.

bodies are now preparing themselves for the forthcoming standardisation process based on the conditions that were set by WRC 2007. The WINNER project contributed to the process in the pre-standardisation phase towards WRC 2007. In this phase, the research work was based on assumptions about the potential outcome of WRC 2007. After WRC 2007, the system concept has to be adapted. For example, the identified frequency bands in WRC 2007 will most probably not be available for exclusive use for mobile and wireless communications. The identified frequency bands provide constraints on the potential carrier bandwidth for IMT-Advanced in order to allow competition between different operators. Therefore, spectrum sharing concepts for the efficient common use of the spectrum will be developed. In addition, further requirements on increased spectral efficiency were raised with respect to the limited available frequency spectrum and bandwidth of identified bands (e.g. in [NGMN]). In parallel with the forthcoming standardisation process, the necessary research activities for adapting and optimising the system concept are continued in the European Eureka Celtic project WINNER+ [WIN+]. These developments and conditions result in the objectives of WINNER+, which are summarised as follows:

r To research and integrate the system concept and to evaluate innovations in areas with a high potential of exploitation in IMT-Advanced based on the LTE evolution and WINNER.

r To harmonise innovations in the pre-standardisation phase. r To contribute to regulatory and standard organisations technology elements that are suitable to IMT-Advanced.

r To participate in the evaluation of selected technology proposals. r To demonstrate the feasibility of selected key technologies.

References [3GPP] [3GPP2]

3rd Generation Partnership Project, www.3gpp.org. 3rd Generation Partnership Project 2, www.3gpp2.org.

Introduction

[ARIB] [BE05] [CDG08] [DARPA] [ECC] [ETRI] [EUC] [FBB05]

[FF] [GSA08] [Hil02] [HT07] [IEEEIO] [IEEESG] [ITU03] [Kle07] [Lee90] [MA06]

[mITF] [mITF03] [mITF04] [mITF05a] [mITF05b] [MK00] [MLM02]

[Moh03a] [Moh03b] [Moh07] [Moh08] [MSK06]

[NGMC] [NGMN] [NSF] [NSZ07]

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ARIB, www.arib.or.jp/english/. Bourse, D. and El-Khazen, K. (2005) ‘End-to-End Reconfigurability (E2 R) Research Perspectives’, IEICE Transactions, 88-B(11):4148–57. CDMA Development Group (2007) ‘Subscriber statistics, end of first quarter of 2008’, www.cdg .org/worldwide/cdma world subscriber.asp. Defense Advanced Research Projects Agency, http://www.darpa.mil/sto/smallunitops/ xg.html. European Commission, http://cordis.europa.eu/home en.html. Electronics and Telecommunications Research Institute, www.etri.re.kr/eng/. Eureka Clusters, www.eureka.be/home.do. Forge, S., Blackman, C. and Bohlin, E. (2005) The Demand for Future Mobile Communications Markets and Services in Europe, Technical Report EUR 21673 EN, Institute for Prospective Technological Studies, Sevilla. FuTURE Forum, www.future-forum.org/en/index.asp. GSM Association (2008) GSM/3G Statistics, end of second quarter of 2008, www.gsacom .com/news/statistics.php4. Hillebrand, F. (2002) GSM and UMTS: The creation of global mobile communication, John Wiley & Sons, Ltd, Chichester. Holma, H. and Toskala, A. (2007) HSDPA/HSUPA for UMTS, John Wiley & Sons, Ltd., Chichester. IEEE-ISTO, www.ieee-isto.org. IEEE Standards Groups, http://grouper.ieee.org/groups/802/. ITU-R (2003) Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000, Recommendation M.1645. Klemettinen, M. (2007) Enabling Technologies for Mobile Services: The MobiLife Book, John Wiley & Sons, Ltd, Chichester. Lee, W.C.Y. (1990) Mobile cellular telecommunications systems, McGraw-Hill Book Company, Singapore. Mohr, W. and Aftelak, A. (2006) ‘The Wireless World Initiative: A Collaborative Approach to Creating the Building Blocks for Systems Beyond 3G’, IEEE Vehicular Technology Conference 2006 Fall, September 25–28, Montréal, Canada. mobile IT Forum, www.mitf.org/index e.html. mobile IT Forum (2003) Flying Carpet: Towards the 4th Generation Mobile Communications Systems, Version 1.00. mobile IT Forum (2004) Flying Carpet: Towards the 4th Generation Mobile Communications Systems, Version 2.00. mobile IT Forum (2005), Recent Mobile Commerce Trends and Perspective: Open Sesame, Version 1.00. mobile IT Forum (2005), 4G Mobile System Requirements Document, Version 1.1. Mohr, W. and Konhäuser, W. (2000) ‘Access Network Evolution Beyond Third Generation Mobile Communications’, IEEE Communications Magazine, 38(12):122–33. Mohr, W., Lüder, R. and Möhrmann, K.H. (2002) ‘Data Rate Estimates, Range Calculations and Spectrum Demand for New Elements of Systems Beyond IMT-2000’, IEEE 5th International Symposium on Wireless Personal Multimedia Communications, October 27–30, Honolulu, Hawaii, USA. Mohr, W. (2003) ‘The Wireless World Research Forum’, Computer Communications, 26(1):2–10. Mohr, W. (2003) ‘Spectrum Demand for Systems Beyond IMT-2000 Based on Data Rate Estimates’, Wireless Communications and Mobile Computing, 3(3):1–19. Mohr, W. (2007), ‘The WINNER (Wireless World Initiative New Radio) Project: Development of a Radio Interface for Systems Beyond 3G’, International Journal of Wireless Information Networks, 14(2):67–78. Mohr, W. (2008) ‘Vision for 2020?’, Wireless Personal Communications, 44(1):27–49. Mrohs, B., Steglich, S., Klemettinen, M., Salo, J.T., Aftelak, A., Cordier, C. and Carrez, F. (2006) ‘MobiLife Service Infrastructure and SPICE Architecture Principles’, 64th IEEE Vehicular Technology Conference (VTC-2006 Fall), September 25–28. Next Generation Mobile Committee, http://ngmcforum.org/ngmc2/eng ver/eng 1.html. NGMN Ltd, www.ngmn.org. National Science Foundation, www.nsf.gov/. Niebert, N., Schieder, A., Zander, J. and Hancock, R. (2007) Ambient Networks: Co-operative Mobile Networking for the Wireless World, John Wiley & Sons, Ltd, Chichester.

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[S91] [Taf05] [Taf06] [TW08]


Scheele, P. (1991) Mobilfunk in Europa, R.v. Decker’s Verlag, G. Schenck GmbH, Heidelberg. Tafazolli, R. (2005) Technologies for the wireless future, John Wiley & Sons, Ltd, Chichester. Tafazolli, R. (2006) Technologies for the wireless future, Volume 2, John Wiley & Sons, Ltd, Chichester. Takagi, H. and Walke, B.H. (2008) Spectrum requirement planning in wireless communications, John Wiley & Sons, Ltd, Chichester. [UMTS05] UMTS Forum (2005) Magic Mobile Future 2010–2020, Report No. 37, www.umts-forum.org/. [Vit95] Viterbi, A.J. (1995) CDMA: Principles of spread spectrum communication, Addison-Wesley Publishing Company, Reading, MA. [Wal02] Walke, B.H. (2002) Mobile radio networks, 2nd Edition, John Wiley & Sons, Ltd, Chichester. [Wal07] Walker, M. (2007) ‘Mobile Broadband: A 2020 Vision’, WWRF meeting #19, Chennai, India, November 5–7. [WIMAX] WiMAX Forum, www.wimaxforum.org/home/. [WIN] WINNER project, www.ist-winner.org/. [WIN+] WINNER+ project, www.celtic-initiative.org/Projects/WINNER+/abstractwinner+.asp. [WWRF] Wireless World Research Forum, www.wireless-world-research.org/.

2 Usage Scenarios and Technical Requirements Marc Werner1 and Paulo Jesus2 1 2

Qualcomm CDMA Technologies GmbH Portugal Telecom Inovaça˜ o

2.1 Introduction This chapter provides an overview of the scenario approach which was adopted to describe the different conditions in which the WINNER radio access networks (RAN) will be deployed and used. Usage scenarios, traffic models and service characteristics motivate the derivation of technical system requirements which are presented in this chapter. These requirements served as a backbone in the design of the WINNER system. As part of the user-centric paradigm that was followed in the WINNER system design, the relevant services were used to form service classes that encompass groups of important applications, taking into account the technical requirements (e.g. throughput, tolerable delays, quality of service (QoS), and mobility requirements) as well as the contextual aspect of the applications. By defining user and usage scenarios, the derivation of service requirements determines technological requirements for the system design. Models for predicting the acceptance and penetration of the WINNER technology in the end-user market can then be made.

2.2 Key Scenario Elements The WINNER system design is governed by the paradigm of communication “any time, anywhere, and with anyone”. While the services offered by the WINNER system will thus be offered ubiquitously, the individual user scenario and context (e.g., age, income, lifestyle, changing professional and personal roles over time, location, practical and social context, terminal type available) will influence the concrete form and mixture of services used. Similarly, usage scenarios can be formulated for a particular service with typical conditions Radio Technologies and Concepts for IMT-Advanced Martin Döttling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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Table 2.1 WINNER scenario types. Key Question User Scenario

Usage Scenario

Location Scenario

Traffic Load Scenario

Deployment Scenario

Which services are needed by a user in a particular role and context? How is a particular service typically used? Which service mix and usage pattern results for a user population in a particular location? Which user distribution, density, and absolute traffic load are generated in a location? Which radio environment dominates and which technical solutions are deployed in a particular location?

Applicability

Averaging

Results

Single user

Instantaneous examples

Services per user type, role and context

Single service and device type Single location

Averaged over time

Typical usage conditions of a service Service mix, mobility, user distribution

Single location

Averaged over time and users

User density, traffic load

Single location


Topology, radio environment, deployment parameters


and environments. Location scenarios go along with a typical user distribution, mobility, and service mix. During the day, different traffic load scenarios are encountered in such a location scenario. Finally, a deployment scenario includes a particular location and traffic load scenario, as well as radio assumptions on the technical solutions that are deployed. Table 2.1 summarises the definition and relationship between the scenario types in the WINNER context. Different user profiles and service demands imply different usage percentages per service or application. The WINNER system concept supports a variety of services, with different requirements, and the end-to-end QoS should be negotiable and controllable. The association of the different scenario elements identifies a certain scenario type. A key scenario can be defined based on user, usage and location scenarios. Key scenarios focus on challenging requirements, e.g. when the defined service classes can be delivered only by technological innovations. Key scenarios are formed by the association of key scenario elements (environment type, coverage range, terminal type, user density and user profile), which can help in the definition of challenging technical requirements, e.g. a minimum cell throughput in a certain service usage, location and deployment context. The following sections list the values that each element can take. In addition to these sections, the following user profiles are considered: old, young, business, tourist, sport.

Usage Scenarios and Technical Requirements

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2.2.1 Environment Type and Coverage Range The environment type is one of the most important elements in the key scenario characterisation. It is related to the physical characteristics of the environment and radio propagation properties:

r dense urban; r metropolitan typical urban; r bad urban; r metropolitan suburban; r rural; r indoor small (office/residential/commercial zones); r indoor to outdoor; r outdoor to indoor; r line of sight (LOS) – stationary feeder; r LOS – feeder; r Rural LOS – moving networks. Different environment types have been defined within the WINNER deployment scenarios (see Section Table 2.5), and are associated with coverage and cell types:

r in building (pico-cell); r hotspot (micro-cell); r rural (micro-cell); r rural (macro-cell); r urban (micro-cell); r urban (macro-cell); r umbrella. Specific environments within Local Area deployment scenarios (office, residential or commercial) are characterised mainly by low terminal mobility, short-range coverage (pico or micro-cells), high density of users and high data-rate applications. Metropolitan Area deployment environments (urban and suburban) normally require ubiquitous coverage (micro or macro-cells) and are characterised by medium–high density of traffic with medium terminal velocity. Wide Area deployment environments are characterised by a continuous and ubiquitous coverage (normally macro-cells), medium–high terminal velocity and a large proportion of low data-rate applications, mainly based on voice.

2.2.2 Terminal Type Different terminal types are envisaged in WINNER key scenarios in order to satisfy end-user requirements and to make use of all available services and applications. The terminal types can be derived from a set of physical characteristics and capabilities such as display size, portability (size and weight), power consumption, maximum transmission power (related to link budget and cell range):

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Table 2.2 User density and traffic parameters for CBR, ABR and UBR applications with multiple session attempts per day in specific environments. (Reproduced by Permission of European Commission, Joint Research Centre © 2005) Deployment scenario Local area

Metropolitan area Wide area

Environment type Indoor small (residential) or dense urban Indoor small (office) or dense urban Indoor small (residential) or suburban Typical urban Suburban Rural

User density (users/km2 )

Number of session attempts per day

Average session duration Dn,m (s)

26 400

5

120

30 000

10

150

6600

6

120

11 000 2000 200

3 10 6

100 120 120

r wearable terminal; r palmtop (PDA); r mobile phone; r laptop; r desktop.

2.2.3 User Density and Traffic Parameters User density depends highly on the environment type and can be defined by service or application type. The most demanding applications experience typical coverage limitations. Table 2.2 characterises the user density and traffic parameters for applications that are likely to have several session attempts per day: constant bit rate (CBR) voice applications, available bit rate (ABR) applications such as VoIP and SMS, and unspecified bit rate (UBR) MMS applications. Table 2.3 characterises the user density and traffic parameters for applications that are likely to have only one session attempt per day: CBR video interactive mobile TV applications, variable bit rate (VBR) applications such as telemedicine, streamed video (sports and events) and web radio, ABR applications such as video streaming, M-commerce and M-banking and UBR e-mail applications.

2.2.4 User Mobility User (or terminal) mobility plays an important role as a key scenario element, since system performance is directly affected in terms of radio resource management, traffic handling, location and QoS management. User mobility requirements have also to be taken into account in system design and architecture.


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Table 2.3 User density and traffic parameters for CBR, VBR, ABR and UBR applications with only one session attempt per day in specific environments. (Reproduced by Permission of European Commission, Joint Research Centre © 2005) Deployment scenario Local area

Metropolitan area Wide area

Environment type

User density (users/km2 )

Average session duration Dn,m (s)

2640

180

15 000

600

4400

180

1100 500 50

120 600 120

Indoor small (residential) or dense urban Indoor small (office) or dense urban Indoor small (residential) or suburban Typical urban Suburban Rural

Table 2.4 presents examples of the mobility ratio in different deployment scenarios and environment types [FBB05] of applications that are likely to have several session attempts per day: constant bit rate (CBR) voice applications, available bit rate (ABR) applications such as VoIP and SMS, and unspecified bit rate (UBR) MMS applications. Applications that are likely to have only one session attempt per day (such as CBR video interactive mobile TV applications, variable bit rate (VBR) applications such as telemedicine,

Table 2.4 Mobility ratio for CBR, ABR and UBR applications in specific environments. (Reproduced by Permission of European Commission, Joint Research Centre © 2005) Deployment scenario Local areaa

Mobility ratio (%) Environment type

0–10 km/h

10–100 km/h

100–250 km/h

>250 km/h

Indoor small (residential) or dense urban Indoor small (office) or dense urban Indoor small (residential) or suburban Typical urban

80

20

0

0

80

20

0

0

80

20

0

0

70

20

10

0

60 70

20 20

20 5

0 5

Metropolitan area Wide area Suburban Rural a

These values do not refer to indoor environments exclusively, but to indoor environments in dense urban and suburban areas [FBB05].

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Table 2.5 WINNER mobility models. Mobility model

Topology

Indoor small (office) environment Hotspot environment

‘Boxy’ – office rooms interconnected by corridors Unrestricted, within confined space

Outdoor to indoor (dense urban) Vehicular environment (typical urban)

Manhattan grid

Rural environment

Unrestricted

Urban and suburban areas outside the city centres, larger macro cells

Movement Stationary; low velocity Low velocity (less than 3 km/h) and stationary with a high probability of changing the direction of movement Low velocity (less than 3 km/h) and stationary Pseudorandom mobility model with semidirectional trajectories; medium to high velocities (50–100 km/h) Pseudorandom mobility model with semidirectional trajectories; high velocities (over 120 km/h)

streamed video (sports and events) and web radio, ABR applications such as video streaming, M-commerce and M-banking and UBR e-mail applications) have similar mobility characteristics (80 % at 0–10 km/h and 20 % at 10–100 km/h) regardless of the environment. Mobility models describe typical terminal movement. Many link and system-level simulators require knowledge of the terminal position in each simulated time step so that the effectiveness of studied techniques such as link adaptation, transmission diversity and beamforming may be evaluated. The WINNER project defined the mobility models listed in Table 2.5. More details can be found in [WIN2D6112].

2.2.5 Deployment Scenarios This section provides a high-level description of the deployment scenarios that were chosen as examples for dedicated system design and performance evaluations of the WINNER system. Further detail on the deployment and parameter assumptions are given in Chapter 13.

2.2.5.1 Wide Area The Wide Area (WA) scenario is meant to reflect the ubiquitous coverage deployment of WINNER in towns and cities, where it will overlap with deployments considered by the metropolitan and local area scenarios. This is similar to today’s macro-cell deployments. Ubiquitous coverage means that users should be able to use mobile communication services whilst on the move. At present, only aircraft are exempt from this expectation. It is assumed that services are provided to users in cars, buses, trains, etc. The most challenging of these


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transportation types is high-speed trains, which may reach speeds of up to 350 km/h. Where relaying is considered as a solution for this case, the in-train deployment is likely to be very similar to the solutions for a single-hop deployment (see Section 2.2.5.3). In the WA context, relaying is important for the communication from the base station (BS) to the train.

2.2.5.2 Metropolitan Area The Metropolitan Area (MA) scenario addresses the WINNER system design and assessment in urban and metropolitan areas where the user density and expected throughput requirements are high. The WINNER system must provide high system capacity in these environments. The MA deployment scenario provides contiguous coverage in urban areas and especially in the centres of large and medium-sized cities. Solutions that enhance system throughput will be prioritised over solutions that provide coverage. Based on these requirements, the Metropolitan Area deployment scenario needs to focus on micro-cellular deployments. In urban environments, base stations and relays are placed clearly below the rooftop level (in suburban deployments, the antenna placement can be above rooftop level). While the micro-cellular deployment helps in reaching the system throughput requirements, it is challenging from the radio propagation perspective. The requirement to support high user density means that MA deployments support outdoor hotspots (the Local Area scenario addresses indoor hotspots), the use of small cells, efficient multi-antenna techniques, and smart interference mitigation and avoidance schemes in order to provide the high system throughput. A basic assumption in the Metropolitan Area deployment scenario is that the deployment is realised outdoors, but outdoor-to-indoor coverage is addressed as well.

2.2.5.3 Local Area The two main environmental settings dominating the Local Area (LA) deployment scenario are the indoor environment and hotspot areas. The user mobility considered within such environments remains low (0–5 km/h), corresponding to a low coverage range (3–100 m). In the case of indoor residential areas, the main assumption is that the wireless link will be provided through a radio access point (RAP) connected to a backbone network. The indoor residential area is considered to be well isolated from neighbouring RAPs, so that there are no interference issues. Therefore, in case of home access, the main implications of the LA scenario relate to:

r deployment of efficient self-organisation schemes in order to allow cost-efficient deployment of many RAPs and to create provision for direct communication in a P2P mode, between WINNER capable terminals within the users’ residences; r efficient hand-over between WINNER deployments so as to allow for user mobility from one coverage area (home) to another (urban); r connectivity provisioning between home RAPs and the WINNER backbone.

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In the hotspot LA scenario, the main high-level assumptions are that this mode will cover larger areas than the ones encompassed in the residential usage and will need more than one RAP, therefore the assumption of limited interference may no longer be valid.

2.3 Service Classes and Service Requirements 2.3.1 Overview of Beyond-3G Applications Table 2.6 represents a list of service classes targeted by the WINNER platform and typical applications that fall into the scope of the respective classes. The typical target data rates, endto-end delays, and bit error rates (BER) have been derived from a user perspective and describe the desired performance at application layer. Because the higher-layer functions influencing these performance figures are out of scope for the definition of a radio access network, these values can only be regarded as guidelines for the derivation of requirements on lower layers. A similar classification of services based on data rate and delay requirements is used by ITU-R, see Chapter 12. All WINNER deployments are generally required to support a set of key services, e.g. real-time applications such as virtual reality and VoIP, and location-based, broadcast/multicast and emergency services.

2.3.2 Requirements for Service Provisioning Services are heterogeneous in nature (that is, they operate with different contents, QoS, delay, data rates, etc.). In fact, because of the heterogeneity of the network, the same service can be provided with different parameters according to a specific location. Certain requirements apply in the context of service provisioning [WIN2D6114]:

r network reconfigurability, content adaptation, and context awareness; r personalisation; r physical context information; r service discovery.

2.3.3 Mapping of Service Requirements to RAN Requirements As mentioned before, the service-level requirements represent the user perspective of a mobile communication system. Because the respective protocol layers that are responsible for user interaction were not in the focus of WINNER, the user-centric requirements need to be transformed into the lower-layer requirements presented in the following sections.

2.3.4 Traffic Models This section describes briefly the most important traffic models for applications and relevant statistical parameters, such as session arrival rate, session duration, packet call size, time between packets calls, etc., that are used as inputs for system-level simulations. For further details (in particular, traffic model parameters), the reader is referred to [WIN2D6137].


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Table 2.6 Service classes and associated applications. Service Class Real-time collaboration and gaming

Applications

Telepresence Videoconference Collaborative work Navigation systems Real-time gaming Geographic real-time datacast Real-time video streaming Collaborative work Short control messages and Alarms signalling Remote control Sensors Presence-driven transfer (lightweight content) Simple interactive applications Presence-driven transfer (heavy content) Interactive geographical maps (remote processing) High-quality interactive Rich data call multimedia Control Video broadcasting or streaming Geographic interactive Video broadcasting multimedia broadcast Streaming Localised map download Interactive ultra-high-quality High-quality video multimedia conference Collaborative work Simple telephony and Voice telephony messaging Instant messaging Lightweight multiplayer games Data and media telephony Audio streaming Video telephony (medium quality) Multiplayer games (high quality)

Data Rate

Delay

BER −6

1– 20 Mbps

highly interactive (200 ms)

10−6

Up to 5 Mbps few seconds (>200 ms)

10−6

5 Mbps

10−6

Up to 30 Mbps Up to 50 Mbps

few seconds (>200 ms) few seconds (>200 ms) few seconds (>200 ms)

−3

10−6 –10−9

10−9 10−6

2.3.4.1 Internet Applications Internet and multimedia traffic can be characterised by frequent transitions between ON and OFF periods, active and inactive states. The ON period corresponds to the file-downloading period and the OFF period corresponds to the user-reading time. In a circuit-switched network, the dedicated bandwidth is wasted during the OFF period. However, packet-switched technology allows higher data transmission rates and uses the bandwidth only during ON periods. Table 2.7 summarises the statistical parameters of various types of Internet traffic.


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Table 2.7 Internet traffic model parameters. (Reproduced by Permission of IEEE © 2009) Application

Traffic Parameters and Distributions

Web browsing (HTTP)

Main object size (SM): Truncated Log-normal Embedded object size (SE): Truncated Log-normal Number of embedded objects per page (Nd): Truncated Pareto Reading time (Dpc): Exponential Parsing time (Tp): Exponential E-Mail Packet inter-arrival time: Poisson ON period length: Weibull OFF period length: Pareto E-mail attachment upload file size: Truncated Log-normal Instant Messaging for Multimedia (IMM) Session inter-arrival time: Exponential Session duration: Exponential Packet call size: Weibull Inactivity time: Pareto

2.3.4.2 Voice over IP Voice over Internet Protocol (VoIP) has emerged as a significant enabling technology and the adoption of industry standards has accelerated its deployment. VoIP technology is generating wide interest across several markets [Amd05]. There is now growing interest in delivering VoIP services over a range of wireless technologies, including 3G, WLAN, WiMAX and systems beyond 3G. VoIP applications require timely packet delivery with low latency, jitter and packet loss values. Three parameters emerge as the primary factors affecting voice quality within networks that offer VoIP technologies: clarity, end-to-end delay and echo. To support interactive voice applications on an Internet protocol (IP) network, four QoS categories must be controlled: bandwidth, latency, Laplacian-distributed jitter and packet loss [KlK01]. A VoIP source file with delay jitter applied is available as [3GPP206b]. 2.3.4.3 Video Telephony Video telephony is full-duplex, real-time, audio-visual communication between or among end users. The concept of video telephony has been available for more than 50 years, but only recently has it come to fruition. The primary challenge facing developers of video telephony is that full-motion, high-resolution video data requires far more bandwidth than audio data. Video telephony is an important but complex service and operators are working hard to promote the flagship service. Two source files are specified for typical video telephony traffic, one for audio and one for video [3GPP206a]. 2.3.4.4 Streaming Streaming applications have constantly been gaining ground in terms of popularity, mainly due to the abundance of bandwidth and hardware sophistication the end-user is experiencing.

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A simplistic video model represents self-similar video traffic with a local Hurst parameter ranging from 0.73 to 0.93, which is true of MPEG video at a rate of 25 fps [IEEE01]. Each video source in this framework is represented by a superposition of two interrupted renewal processes (IRP). The sojourn time in both (ON and OFF) states is Pareto distributed. Recent work [LaH01] on modelling of RealAudio, which is the most popular format for streaming audio applications, has indicated the multi-scale variance of this type of traffic. More specifically, in scales of tens of seconds, a single streaming audio flow has a constant rate while in smaller scales it behaves like a bursty ON–OFF source, with the OFF periods appearing in multiples of approximately 1.8 seconds; this bursty behaviour is also noticed in aggregate streaming audio flows. Other important characteristics incorporate the approximately fixed packet size and the strong correlation of flow requests with the time of the day. The main assumptions of the streaming audio model are the Pareto-distributed session duration and an exponentially distributed session inter-arrival time.

2.3.4.5 File Transfer [3GPP204] proposes a straightforward file transfer (FTP) traffic model. It incorporates two main parameters that describe the behaviour of an FTP transaction, namely the exponentially distributed reading time (the time between successive downloads of the same user) and the truncated log-normal distribution of the file size to be transferred.

2.3.4.6 Interactive Applications Interactive applications comprise a big part of current and forecast data exchanges. The popularity of online entertainment applications such as Internet based gaming is increasing at a fast pace. Other applications include telepresence, telesurgery and e-learning services. The topic of Internet game traffic modelling is relatively new and literature on this issue is scarce. However, Borella has done some important work on source models of network game traffic [Bor00] and Farber on the validation of the proposed traffic model on new, evolved and more demanding versions of games [Far02]. For a mathematical description of the distribution functions for inter-arrival time or packet size, it is necessary to find a function of similar shape and fit its parameters to empirical data. [Bor00] identified the Extreme Value distribution to fit best for specific game traffic and this function was also chosen in [Far02]. Functions similar to shifted log-normal or shifted Weibull also lead to acceptable fits.

2.4 Requirements for System Capabilities The WINNER concept offers significantly enhanced system capabilities compared to 3G systems. Additionally, basic capabilities fulfilled by legacy systems and relevant to the stakeholders have to be maintained. In this section, requirements are formulated that describe the minimum WINNER system capabilities. The requirements reproduced from the official WINNER requirements [WIN2D6114] are shown in a box.


25

A fundamental requirement of a cellular system is its autonomy: WINNER shall be self-contained, allowing it to target the chosen requirements without the need for inter-working with other systems.

2.4.1 Generalised Mobility Support within WINNER A mobile radio system should be able to provide the user with a continuous connection regardless of the user’s location or movement from one cell to another. This support for mobility is one of the key features of a mobile system. In the WINNER system, where different deployment modes will operate together, it can support users moving from one operational mode to another or even to a different network, without losing their connection, by performing handovers. In WINNER there will be different types of handover, depending on whether the user is changing frequency, cell, mode, or network. In order to initiate a handover, many types of trigger are defined. The technical solutions to address these mobility requirements are considered in Chapters 4 and 10. The handover process shall respect and take into account the user’s service requirements especially in terms of delay, so the handover will not be noticeable at the application level. WINNER shall be able to provide the user terminal a list of neighbouring cells for which it should perform measurements in order to improve the handover process. These cells could be operating in a different mode or another network. WINNER shall be able to support not only radio but also IP handover for the user. Both radio and IP handovers shall be seamless and not noticeable by the end user. WINNER shall be able to support seamless intra-deployment handover of user sessions (or flows) inside the same RAP or between RAPs of one deployment mode (LA, MA or WA). WINNER shall be able to route each flow individually through the available deployments. This must be done taking into account the flow’s QoS requirements, the user’s preferences, and the capabilities, advantages and disadvantages of each deployment. WINNER shall support seamless handover of any individual flow between cells of different deployments. WINNER shall support resource management between deployments, including handover and load balancing between base stations of different deployments.

2.4.2 Generalised Mobility Support between WINNER and Legacy Networks The WINNER system shall provide an interface that supports cooperation with legacy systems in order to provide efficient interworking between WINNER radio access technologies (RATs) and legacy RATs. These RATs could belong to the same or

26


different operators. This interface shall also support the seamless handover between WINNER and legacy networks.

2.4.3 Measurement Requirements for the WINNER System Measurements are essential inputs for radio resource management (RRM) algorithms and therefore mechanisms to configure, perform and report measurements must be defined for the WINNER system. Measurements affect the design of the PHY, MAC and RLC (or RRC) layers, from the physical procurement of the measurements to the transport of the measurements to the logical entities that need this information, including the definition of the protocols for the transport of the information. The WINNER system shall provide the RRM (or cooperative RRM) entity with a set of measurements for handover and other RRM functionalities. These measurements may include:

r The received signal strength, interference level and carrier-to-interference (C/I) ratio. These must allow a conclusion on the reception quality of the current flows and the possibility (or necessity) of doing a handover to other cell or radio access technology. In WINNER, these measurements will be based on the uplink (UL) and downlink (DL) synchronisation pilots (performed by terminals (UT), base stations (BS) and relay nodes (RN)), not only in the WINNER RAN but also on legacy RANs, when necessary. Three types of measurement should be available: intra-frequency, inter-frequency and inter-system (which should be performed by WINNER multi-system terminals). r The transmitted power setting at a precise moment. Path loss measurements can also be measured as the difference between the transmitted power and the received signal strength. For WINNER, this measurement should be performed by UT, BS and RN. r Quality measurements. These must allow statements on the quality perceived by the UT and RAN and comparison with the required quality. So it is necessary to do some measurements based on users’ flows in order to determine QoS levels and compare them with thresholds. QoS indicators could be the block error rate (BLER), the retransmitted block rate and the bit rate at different layers. r The cell load, which corresponds to the difference between available and used resources. Cell load can be measured at different levels, e.g., transmitted radio power (PHY layer), number of used chunks (MAC user plane), etc. r Terminal velocity and location. As a minimum requirement, the system should know the serving BS to which the UT is attached. To estimate the coverage area of the serving BS, a more detailed position should be determined using received signal strength measurements or satellite measurements (e.g. GPS). r A list of neighbouring base stations and relay nodes. During the inter-system handover from a legacy system to a WINNER cell, the target WINNER deployment mode and cell for the user performing the handover should be found. The selection should be based on the parameters that the current deployment mode or RAT


27

and possible target deployment mode or RATs can offer to the terminal. Other information elements will be taken into account:

r Network capabilities: The legacy RANs cannot support many of the services that can be offered by networks beyond 3G (B3G), but B3G networks with limited bandwidth can only provide a limited subset of services. Information on network capabilities should be available (data rate, delay, etc). r Terminal capabilities: B3G terminals will be classified by performance (e.g., delay and data rates). It is expected that some terminals will have limited performance to achieve reduced size, longer battery duration, lower cost, etc. r User preferences: The user may select some characteristics, e.g., by pre-selecting the network that offers a lower cost per transmitted bit. r Operator classification of users: The operator could offer different classes of subscription (e.g. bronze, silver and premium) with different levels of performance. r Other high-level parameters: The architecture should be flexible enough to accommodate other information elements. In the WINNER system, UT, BS and RN should perform measurements, and the mechanisms to report these measurements to the UT, RN and admission control server (ACS) should be established. The measurements should be triggered periodically and on demand. The WINNER multi-system terminal should have the possibility of measuring the received signal strength of base stations and access points of legacy systems. Moreover, the RRM entity in WINNER, associated with the BS, should have the possibility of knowing through signalling and when necessary, the cells of legacy systems to which a fast intersystem handover is feasible. Most of the measurements described in the previous section are required not only for WINNER but also for the legacy networks. The possible measurements and signalling parameters include:

r received signal strength, interference level and C/I ratio; r transmitted power; r cell load; r terminal locations and velocities. These figures will be signalled either directly from the legacy network or reported to the WINNER RAN by the terminals. The WINNER system concept shall provide an interface to legacy RATs that supports seamless handover to minimise degradation of communication quality and enables further cooperation mechanisms, e.g., RRM. The WINNER concept shall include a signalling channel to report measurements of other RANs. In order to enable measurements of other systems by a WINNER transceiver, the WINNER MAC frame design shall allow for the reservation of time-frequency resources which are deliberately not used for transmission, but left free for measurements.

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The WINNER concept shall include a signalling channel to notify user terminals about other available RANs.

2.4.4 Support for QoS Mechanisms and Prioritisation of Flows The service classes introduced in Table 2.6 exhibit different target values of performance indicators. These targets are derived from general QoS requirements on the service level, which should be propagated through all layers of the WINNER protocol stack. Packet flows are classified by QoS requirements. The QoS of each flow shall be controlled individually. QoS related to the delivery of a requested application shall be negotiable, including renegotiation during an active session. To facilitate service-motivated QoS support mechanisms, prioritisation of user data flows should be applied to services with tight delay constraints (e.g., voice and interactive multimedia services). Usually, some timestamp information is available for incoming data related to such services (for VoIP, the RTP could be used). WINNER should be able to evaluate such information and to configure the prioritisation of the respective flows so that the data is available at the receiver within delay bounds. On the other hand, if the timestamp indicates that the delay limit has been already exceeded, the packet can be discarded in the network without consuming radio resources. WINNER should be able to signal prioritisation information to the external networks to which its outgoing flows are routed. Prioritisation of user data flows should also be applied to services with data rate constraints (e.g., real-time video streaming). Counters could be implemented at various protocol layers to ensure that the flow’s data rate remains between certain limits. Activated at certain thresholds, prioritisation mechanisms may lead to a rate stabilisation even for an increasing network load. Prioritisation mechanisms should be realised on different protocol layers:

r PHY: transmit power control, modulation and coding format for the subcarriers that carry a prioritised flow;

r MAC: resource scheduling algorithm; r RLC/RRC: RRM mechanisms such as admission control and load control. WINNER shall support prioritisation of flows to facilitate QoS support. Prioritisation mechanisms shall be implemented at all necessary protocol layers. Chapter 4 provides an overview of the QoS and flow class handling and its implementation in the logical node architecture of WINNER.

2.5 Terminal Requirements Today, many different kinds of terminals for mobile communication systems are available. In next generation networks, many more will exist with different sizes, shapes and functionalities.


29

A wide range of different applications and services implies terminal heterogeneity, i.e., support of terminals that differ in display size, weight and dimensions, energy consumption, and complexity. The communication interface between human user and devices, if required, should be as natural as possible – like human talking or conversation – supporting audiovisual and other natural means of information exchange. Furthermore, high definition and quality video applications provided by broadcast or multicast (e.g., virtual sightseeing) will require, on the terminal, high performance in video processing (i.e. codecs) and display capabilities accompanied by extended autonomy and battery life. WINNER shall support different terminal classes, in order to accommodate terminals with a wide range of complexity, costs, capabilities and form factors. Maximum and average transmit power in terminals must comply with the EMF regulations for the corresponding frequency range. Talk and standby times shall be at least comparable to, and preferably better than, 2G and 3G systems. Power consumption shall be minimised in order to improve battery life. The WINNER system shall be able to make use of antenna diversity or multi-antenna processing techniques at the terminal to improve signal-to-noise ratio, data rates and system capacity. The WINNER system shall be able operate with basic functionality if only one transmit antenna is used at the terminal to save transmit power, complexity and cost. As a consequence, power-saving functionalities have been included in both radio resource control (RRC) states, and frame design includes the possibility for micro-sleep (see Chapters 10 and 9, respectively). The WINNER versatile multi-antenna processing scheme, including single-antenna operation, is explained in detail in Chapter 7.

2.6 Performance Requirements From the end user’s point of view, performance requirements can be derived from the supported services. Measuring the system performance has many aspects and the measurement criteria have to be selected in a way that the ‘real’ user experience is reflected. The user experience includes quantitative factors, e.g., the time to transfer a file of a certain size, the time to download a whole web page, the minimum response time for gaming applications or streaming services, and also qualitative assessments and feelings, e.g., about the voice quality. Note that the end-user experience is independent of the underlying transport protocol; the user doesn’t care whether FTP, HTTP or any other protocol is used. On the other hand, the measures should not only reflect the end user’s experience but should also measure the system performance, e.g., from an operator’s point of view. For the operator, the commercial operation of a mobile radio network and the usage of spectrum bands is coupled to significant costs, thus the operator’s target is to exploit the frequency spectrum

30


efficiently by providing a full range of services to a large number of users per radio cell, with appropriate quality of service. The resulting technical performance requirements complement the requirements driven by the end-user experience. This section focuses on the technical performance requirements of the WINNER radio access network, covering all protocol layers below IP level. The performance of higher layers elements and functions (which also influence the end-user experience) was not considered in the technical work within WINNER. However, the WINNER RAN should be designed in such a way as not to prevent the fulfilment of service requirements (see Section 2.3). Therefore, certain higher-layer requirements that have an influence on the lower-layer performance requirements are addressed here.

2.6.1 Coverage Coverage is of major concern for WINNER. Therefore coverage aspects are inherently included in various other performance figures. A simple and viable way for a simulation to include coverage aspects in performance targets in loaded network conditions is to define the target values for a definite point in the cumulative distribution function of the respective performance measure. Within WINNER, the 95th percentile is used. Although no strict one-to-one mapping of the 95th percentile to coverage area is possible, it can be regarded as the target that will be exceeded in the main service area of a site, whereas the remaining 5% represent unfavourable situations, such as users in heavy shadowing at the cell edge. In order to ensure coverage and user satisfaction, a “satisfied-user criterion” has to be adopted in the definition of certain performance requirements, such as the spectral efficiency in typical, loaded conditions. The target spectral efficiency has then to be proven in an operational point that guarantees both coverage and user satisfaction. A detailed description of this satisfied-user criterion is given in Section 13.5. However it is necessary to keep in mind that the satisfied-user criterion, and therefore the coverage, depends on the type of application. As examples, the throughput and delay figures given for the service classes in Table 2.6 might serve as individual satisfied-user criteria.

2.6.2 Data Rate 2.6.2.1 Definition of User Throughput User throughput is defined as the throughput of correctly received information bits at IP packet layer during packet calls (known as ‘active session throughput’), for a specific link direction (uplink or downlink). The user throughput shall be measured considering all effects of packet loss and retransmissions, and taking into account the overhead due to guard bands, guard times, preambles, pilots, headers, and control signalling. Only the information bits in correctly received packets shall be counted. Packets are considered not correctly received if in error (e.g., failure of CRC or equivalent measures) or if they arrive with excessive delay compared to a service specific threshold. Impact of functions related to header compression, encryption, ciphering, and transport delays between base station and gateway shall not be considered, as these do not characterise the performance of the radio access technology itself.


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2.6.2.2 Peak Data Rate Peak data rate is the user throughput (according to the definition in Section 2.6.2.1) measured in ideal transmission conditions in a system which concentrates all of its resources on one user; it describes the fundamental performance limits due to system design. The WINNER system design shall achieve a downlink peak data rate of 100 Mbps in macro-cell deployments. The WINNER system design shall achieve a downlink peak data rate of 1 Gbps in local-area deployments. These requirements assume a 100 MHz system bandwidth allocation where half of the radio resources are assigned to each of uplink and downlink.

2.6.2.3 Sustainable Data Rate The sustainable data rate offered by a system is typically defined as the average user throughput achieved over the typical period of activity of a service in a cellular environment. This also requires definition of the number of simultaneous users that can be offered with the respective data rate under a service-dependent satisfied-user criterion. These figures are highly dependent on the deployment scenario, service and distribution of users. Assumptions include a realistic load in neighbouring cells (i.e., inter-cell interference) and the distribution of resources between users in the serving cell. Instead of defining the sustainable date rate, the spectral efficiency is measured for the maximum number of users that can be provided with a certain service fulfilling a specific satisfied-user criterion (see Section 2.6.5).

2.6.3 Allowable Error Rate As with most of the system requirements, the guarantees in terms of allowed residual error rates at the application layer also stem from the service classes table. The most demanding applications in terms of permissible error rates relate to critical functions and security transactions, such as remote control, authentications for remote payment and financial transactions, etc. In many packet-based transmissions, virtually error-free communication will be realised by means of reliable error detection and hybrid automatic repeat–request (ARQ) techniques, transforming transmission errors into additional delay. Therefore, no formal requirements regarding error rates are defined.

2.6.4 Delay 2.6.4.1 Definition of User-Plane Packet Delay The user-plane (U-plane) packet delay is defined in terms of the length of the one-way transmission time (from when a packet is available at the IP layer in the user terminal until when it is available at the IP layer in the base station, or vice versa). User-plane packet delay includes delay introduced by associated protocols and control signalling, assuming the user terminal

32


is in the active state. The impact of functions related to header compression, encryption, ciphering, and transport delays between base station and gateway are not considered. 2.6.4.2 Achievable User-Plane Packet Delay The user-plane packet delay measured in ideal transmission condition in an unloaded system is known as the ‘achievable user-plane packet delay’. Performance is evaluated for a single user per cell, so as to describe the performance limits due to system design. The achievable delay is defined for a single-hop transmission of a minimum size of IP packet. The WINNER system shall enable an achievable user-plane packet delay of less than 1 ms in the downlink and 2 ms in the uplink of a single-hop transmission in unloaded conditions. The WINNER system shall enable an achievable user-plane packet delay of less than 5 ms in a two-hop transmission in unloaded conditions. These delay targets enable two features that give the WINNER system unique properties:

r Link retransmission (hybrid ARQ) can be used for flows from traffic classes with the most stringent delay requirements and delay jitter requirements. This results in improved link quality, as seen from higher layers. r Channel-aware link adaptation and scheduling can be used with respect to frequency selective fading channels, using channel quality information prediction and feedback, at vehicular velocities. This improves the performance by adaptation and multi-user scheduling gains. The QoS framework will provide guarantees for services that require high interactivity (a delay in the network level of less than 20 ms).

2.6.5 Spectral Efficiency Spectral efficiency is a performance measure used in widely different contexts throughout the literature. It is sensitive to a number of assumptions and parameters, including cell range, bandwidth, transmit power, sectorisation, antenna configuration, re-use factor, terminal capabilities, user mobility, number of users, and cell load. Furthermore, control overhead and real-world effects, such as imperfect synchronisation, channel estimation, control signalling and link adaptation, need to be considered. Peak spectral efficiencies can easily be calculated by dividing the peak data rate that can be carried per cell (see Section 2.6.2.2) by the system bandwidth. However, these figures are the result of the supported code rate, modulation, and spatial multiplexing. These are, therefore, simply the upper limits supported without any indication of whether and how frequently in practical deployments these values are reached. Relevant figures can be found in [WIN1D71]. For realistic comparison of air interface performance and for support of realistic economical projections, spectral efficiency has to be measured at an operational point of the system that


33

ensures sufficient user satisfaction. Therefore the ‘satisfied-user criterion’ plays a crucial role in determination of spectral efficiency. In this section, the term spectral efficiency is used only with the definition including the satisfied-user criterion. In other texts this is also referred to as ‘achievable spectral efficiency’ or ‘average spectral efficiency’. These definitions of spectral efficiency should not be confused with peak spectral efficiency. The spectral efficiency is defined as the sum of user throughputs for all user terminals served by a certain radio cell, divided by the overall system bandwidth per link direction (uplink or downlink), calculated for the maximum load (number of users) that still allows fulfilling the satisfied-user criterion of a selected service in terms of data rate and delay. Furthermore, a sufficient statistic (the averaging effect) is needed in order to avoid singularities and misinterpretation. The satisfied-user criterion for WINNER phase II has been defined in [WIN2D6111] as 95% of the users having an average active session throughput greater than or equal to 2 Mbps. It was decided to formulate the required spectral efficiency per cell and not per site. In most deployments, the spectral efficiency per site can easily be increased by installing more independent radio sectors per base station. Furthermore, the definition per cell is in line with most standardisation and requirement definition activities. Compared to a definition per site, the efficiency figures given below should be multiplied by the number of sectors per site because, in most deployments, each sector represents an independent radio cell. As in [3GPP07], a cell is defined by a unique identification that is broadcast over a geographical area by a WINNER RAP. However, it is assumed that a relay node does not constitute an independent cell but adds to the capacity of its serving base station cell (this concept is also reflected in the widely used term ‘relay-enhanced cell’). WINNER shall provide a spectral efficiency in connected sites of 2 bps/Hz/cell for the downlink and 1 bps/Hz/cell for the uplink in wide-area deployments at an operation point that fulfils the satisfied-user criteria. WINNER shall provide a spectral efficiency in connected sites of 3 bps/Hz/cell for the downlink and 1.5 bps/Hz/cell for the uplink in metropolitan-area deployments at an operation point that fulfils the satisfied-user criteria. WINNER shall provide a spectral efficiency of 10 bps/Hz/cell for the downlink and 5 bps/Hz/cell for the uplink in isolated (non-contiguous) sites (i.e., the local area) at an operation point that fulfils the satisfied-user criteria. The above spectral efficiency figures are valid for typical WINNER deployments, e.g., a cellular deployment with a site-to-site distance of approximately 1 km and some moderate multi-antenna technology in a wide area; a deployment covering the Manhattan street grid with the help of relay nodes in the metropolitan area; and advanced MIMO deployment using remote radio heads in a local area [WIN2D6137]. It is important to understand that a direct comparison of spectral efficiency values (e.g., between WINNER and other systems) is only possible if the same satisfied-user criteria have

34


been used. In particular, many results in research and standardisation use less challenging satisfied-user criteria (or even none at all), which is the main reason for high spectral efficiency values.

2.6.6 Maximum Terminal Speed The maximum user terminal speed is limited by the variability of the radio channel due to Doppler spread or shift related to the deployment scenarios and service type considered. Taking into account the wide-area deployment scenarios and assumptions, the challenge is to allow reliable links to high-speed trains at velocities up to 350 km/h or even up to 500 km/h, depending on the carrier frequency band and deployment environment. WINNER shall support at least low-rate services with reduced cell capacity for terminal speeds of up to 350 km/h, or even up to 500 km/h, depending on the Doppler shift related to the carrier frequency band and deployment environment.

2.7 Spectrum Requirements 2.7.1 WINNER Spectrum Range The WINNER system shall be able to operate anywhere in the range 2.7–5.0 GHz, taking into account that WINNER target characteristics have to be met.

2.7.2 Utilisation of Current Mobile Service Bands There may be situations where systems in current mobile bands need to be migrated into WINNER. Therefore the WINNER system concept needs to be able to utilise currently available bands for cellular use (i.e., 800–900 MHz, 1800–1900 MHz, 2 GHz and 2.6 GHz frequency ranges or other frequency bands that may become available below 2.7 GHz). Such a situation may arise from technical, commercial or regulatory reasons. In this case, due to the limited bandwidth, the forecast highest bit rate services will not be possible and the performance of the networks might be limited. The WINNER system shall be able to utilise current bands for cellular communication.

2.7.3 Spectrum Fragmentation From an implementation point of view, a channel bandwidth of 100 MHz is the currently agreed upper limit. This relatively broad channel bandwidth decreases the likelihood of achieving a sufficient spectrum assignment for a WINNER network or a number of WINNER networks in one single chunk. Available spectrum bandwidth may be fragmented due to practical geographical spectrum allocation. Indeed, some of the bands are expected to be available on a global basis while others might be available only in certain regions or countries. In this case, the WINNER system concept should be able to support a fragmented spectrum.


35

The WINNER system shall be able to handle fragmented spectrum assignments efficiently.

2.7.4 Coexistence with Other Systems The WINNER system shall be able to coexist with non-WINNER systems and to share resources with minimal mutual interference. Operation in unlicensed bands shall be possible. Performance requirements shall be met if the interference caused by other systems operating in the same band does not prevent this.

2.7.5 Sharing Spectrum between WINNER RANs Significant advantages are expected, especially during the network deployment phase or if there is a severe lack of spectrum, when the spectrum can be shared between multiple parallel RANs using the same WINNER RAT and providing similar services. Most of the advantages result from the enhanced spectral scalability of the system since it should allow:

r the deployment of multiple RANs at the launch of the system, even when the spectrum can be made available gradually according to increasing traffic demands;

r system flexibility towards geographical differences in regulatory spectrum assignments; r more versatile operation of the networks; r adaptation of the spectrum available to a network to reflect changes in the number of subscribers as well as in daily traffic patterns.

The WINNER system shall be able to use spectrum shared between parallel network deployments (flexible spectrum utilisation).

2.7.6 Sharing Spectrum between Cell Layers of a WINNER System Further advantages are expected, especially during the network deployment phase or if there is a severe lack of spectrum, when the spectrum can be shared between different cell types of a WINNER network. The WINNER system shall be able to use spectrum shared between different cell types, e.g., between macro cells and micro cells, or between micro cells and hotspots. WINNER features addressing these spectrum requirements, such as operation in fragmented spectrum, spectrum sharing and flexible spectrum use, are treated in Chapter 11.

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2.7.7 System Bandwidth The maximum required physical data rate and other performance requirements determine the necessary system bandwidth. Due to implementation complexity and power consumption, an upper bound for the maximum continuous bandwidth of one radio link is assumed. A radio link is here defined to include all physical connections in a continuous frequency range between a terminal and the radio access network. This is intended to be the case for both frequency-division duplex (FDD) and time-division duplex (TDD) cases. The maximum required bandwidth for one radio link is 100 MHz. The upper bandwidth limit can be further explained by two aspects: the receiver channelselection filter (or automatic gain control (AGC)) integration and the analogue–digital converter (ADC) implementation. Regarding the filter, implementation issues arise for very high bandwidths with basic operational amplifier methods and the power consumption of this filter starts to increase. In the ADC, the bit count (number of bits), bandwidth, and required maximum modulation order (depth) are dependent on each other: the required sampling rate is at least twice the bandwidth. The total system bandwidth needed to fulfil the requirements should be minimised. Migration to systems beyond 3G requires the ability to operate in the spectrum bands made available in different regions of the world. A minimum required bandwidth is assumed for one duplex radio link. Performance in the minimum bandwidth may be significantly reduced from that in a ‘typical’ bandwidth. A minimum bandwidth of 1.25 MHz for TDD and 1.25 MHz per duplex FDD link, i.e., 1.25 MHz for uplink and 1.25 MHz for downlink, is required.

2.8 Dependency of Requirements It is clear that the different requirements presented in the previous sections are not mutually independent. Instead, the service-level requirements typically depend on the requirements of intermediate system layers to facilitate a certain QoS level. Figure 2.1 illustrates a mapping of higher-layer to lower-layer requirements. The dependencies indicated in the figure should be regarded as examples and are not exhaustive.

2.9 Conclusion As a starting point and motivation for the design of WINNER as a beyond-3G (B3G) mobile communication system with superior performance, this chapter provided a synopsis of possible usage scenarios for B3G services. Key scenario elements include the environment type, terminal type, and user mobility. With respect to system deployment, three scenarios were introduced as examples for detailed system design and performance evaluation: local area, metropolitan area, and wide area. The deployment and configuration of WINNER will


service-level/ higher-layer requirements

37

service classes virtual reality

real-time applications

personalisation

security/privacy

service-level delay 0 is the number of floors between the BS and the MS). is the B1 path loss, PLC2 is the C2 path loss, dout is the distance between the outdoor terminal and the point on the wall that is nearest to the indoor terminal, din is the distance from the wall to the indoor terminal, θ is the angle between the outdoor path and the normal of the wall. nFl is the floor index (the ground floor has index 1). c d’ 8 BP = 4 h’BS h’MS fc /c, where fc is the centre frequency in Hz, c = 3.0 × 10 m/s is the propagation velocity in free space, and h’BS and h’MS are the effective antenna heights at the BS and the MS, respectively. The effective antenna heights h’BS and h’MS are computed as follows: h’BS = hBS – 1.0 m, h’MS = hMS – 1.0 m, where hBS and hMS are the actual antenna heights, and the effective environment height in urban environments is assumed to be equal to 1.0 m. d The distances d and d are defined in Figure 3.20. 1 2 e The break point distance, d , is computed as follows: d BP BP = 4 hBS hMS fc /c, where hBS , hMS , fc and c have the same definition as in Note c.

NLOS

LOS NLOSb

C2

NLOS

LOS NLOSa

NLOSa

LOSa

B1

A1

Scenario

Table 3.7 Summary of the WINNER path-loss models.

WINNER II Channel Models

77

+

MS

d2

d1

BS

d2

−

Figure 3.20 Geometry for d1 and d2 path-loss model in scenario B1. (Reproduced with the kind permission of ITU).

3.4.3.2 Transitions Between LOS and NLOS Conditions The WINNER channel model allows transitions between different propagation conditions, the most important of which are transitions between LOS and NLOS within the same WINNER scenario. In the A1 (indoor) and B1 (urban microcell) scenarios, transitions from LOS to NLOS can occur as a result of the MS turning from the corridor or street in which the BS is located (the LOS corridor or street) into a perpendicular corridor or street. An analysis of this specific case has indicated that such transitions can be adequately modelled by using the A1 or B1 LOS and NLOS path-loss models defined in Table 3.7. Let d1 and d2 denote the distances along the LOS corridor or street and the perpendicular corridor or street, respectively, as illustrated in Figure 3.20. The A1 LOS path-loss model is then considered to be applicable for values of d2 smaller than 3F 1 , where F 1 represents the radius of the first Fresnel zone (for a definition of Fresnel zones see [Sau99, Section 3.3.1]). For values of d2 greater than 3F 1 , the A1 NLOS path-loss model can be used. For the B1 scenario, a better fit to measured data was obtained by choosing the NLOS/LOS transition distance equal to 10F 1 . It is noted that, in most cases, reasonably good results can also be obtained by setting the transition distance equal to half the width of the LOS corridor or street, as reflected by the path-loss model for B1 NLOS in Table 3.7.

3.4.4 Values for Generic Channel Models Table 3.8 provides parameter values corresponding to the WINNER generic channel models. Parameter values related to elevation angles are provided in Table 3.9.

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Cross-Correlationsc

Delay spread (DS) log10 ([s]) AoDa spread (ASD) log10 ([◦ ]) AoAa spread (ASA) log10 ([◦ ]) Shadow fading (SF) [dB] K-factor (K) [dB]

Scenarios

µ σ µ σ µ σ σ µ σ ASD vs DS ASA vs DS ASA vs SF ASD vs SF DS vs SF ASD vs ASA ASD vs K ASA vs K DS vs K SF vs K

Table 3.8 Parameters for generic models.

−7.42 0.27 1.64 0.31 1.65 0.26 3 7 6 0.7 0.8 −0.5 −0.5 −0.6 0.6 −0.6 −0.6 −0.6 0.4

LOS

A1

−7.60 0.19 1.73 0.23 1.69 0.14 4 N/A N/A −0.1 0.3 −0.4 0 −0.5 −0.3 N/A N/A N/A N/A

NLOS −7.39 0.36 1.25 0.42 1.76 0.16 7 N/A N/A 0.4 0.4 0.2 0 −0.5 0 N/A N/A N/A N/A

NLOS

B4

−7.44 0.25 0.40 0.37 1.40 0.20 3 9 6 0.5 0.8 −0.5 −0.5 −0.4 0.4 −0.3 −0.3 −0.7 0.5

LOS

B1

−7.12 0.12 1.19 0.21 1.55 0.20 4 N/A N/A 0.2 0.4 −0.4 0 −0.7 0.1 N/A N/A N/A N/A

NLOS −7.23 0.49 0.78 0.12 1.48 0.20 4/6b 9 7 0.2 0.8 −0.5 −0.5 −0.6 0.1 0.2 −0.2 −0.2 0

LOS

C1

−7.12 0.33 0.90 0.36 1.65 0.30 8 N/A N/A 0.3 0.7 −0.3 −0.4 −0.4 0.3 N/A N/A N/A N/A

NLOS −7.39 0.63 1 0.25 1.7 0.19 4/6b 7 3 0.4 0.8 −0.5 −0.5 −0.4 0.3 0.1 −0.2 −0.4 0.3

LOS

C2

−6.63 0.32 0.93 0.22 1.72 0.14 8 N/A N/A 0.4 0.6 −0.3 −0.6 −0.4 0.4 N/A N/A N/A N/A

NLOS

−7.80 0.57 0.78 0.21 1.20 0.18 4/6b 7 6 −0.1 0.2 −0.2 0.2 −0.5 −0.3 0 0.1 0 0

LOS

D1

−7.60 0.48 0.96 0.45 1.52 0.27 8 N/A N/A −0.4 0.1 0.1 0.6 −0.5 −0.2 N/A N/A N/A N/A

NLOS

−7.4 0.2 0.7 0.31 1.5 0.2 4 7 6 −0.1 0.2 −0.2 0.2 −0.5 −0.3 0 0.1 0 0

LOS

D2a

79

ζ [dB] DS ASD ASA SF K

10 4 16 20 5 5 3 4 5 3 4 N/A

11 4 12 20 5 5 6 7 6 2 6 6

2.4

8 5 4 10 11 17 7 N/A

9 11 12 20

2.2

Exp

NLOS

B4

3 18 3 9 13 12 14 10

9 3 8 20

3.2

Exp

LOS

B1 LOS

10 22 3 8 10 9 12 N/A

8 3 16 20

1 N

5 5 3 6 15 20 40 10

8 4 15 20

2.4

Uniform Exp ≤800 ns

NLOS

C1

2 10 3 40 30 30 50 N/A

4 3 14 20

1.5

Exp

NLOS

6 12 3 40 15 15 45 12

8 4 8 20

2.5

Exp

LOS

C2

2 15 3 40 50 50 50 N/A

7 3 20 20

2.3

Exp

NLOS

2 3 3 64 25 40 40 40

12 8 11 20

3.8

Exp

LOS

D1

2 3 3 36 30 40 120 N/A

7 4 10 20

1.7

Exp

NLOS

b

For determining the arrival and departure directions, we consider the downlink case, i.e. departure refers to BS and arrival refers to MS. The path loss models for the C1 LOS and D1 LOS scenarios contain separate shadowing standard deviations for distances smaller and greater than the breakpoint distance, respectively. c The sign of the shadow fading term is defined so that increasing values of SF correspond to increasing received power at the MS.

a

Number of clusters Number of rays per cluster Cluster ASD Cluster ASA Per cluster shadowing std Correlation distance [m]

µ σ

3

Wrapped Gaussian

AoD and AoA distribution Delay scaling parameter rτ XPR [dB]

Exp

Exp

Delay distribution

NLOS

LOS

Scenarios

A1

2 3 3 64 25 40 40 40

12 8 8 20

3.8

Exp

LOS

D2a

80


Table 3.9 Elevation-related parameters for generic models. A1 Scenarios Elevation AoD spread (ESD)a Elevation AoA spread (ESA)a Cross-Correlations

µ σ µ σ ESD vs DS ESA vs DS ESA vs SF ESD vs SF ESD vs ESA

Elevation AoD and AoA distribution Cluster ESD Cluster ESA

B4

LOS

NLOS

NLOS

0.88 0.31 0.94 0.26 0.5 0.7 −0.1 −0.4 0.4 Gaussian

1.06 0.21 1.10 0.17 −0.6 −0.1 0.3 0.1 0.5 Gaussian

1.01 0.43 0.88 0.34 N/A 0.2 0.2 N/A N/A Gaussian

3 3

3 3

3 3

a

ESD and ESA refer to elevation angle spreads at the outdoor and indoor terminals, respectively.

System-level simulations require estimates of the probability of line of sight. For scenarios A2, B2, B4, C2 and C3, the LOS probability is approximated as zero. For the remaining scenarios, LOS probability models are provided in Table 3.10. These models are based on relatively limited data sets or specific assumptions and approximations regarding the location of obstacles in the direct path and should not, therefore, be considered exact. If the terminal locations are known with respect to a street grid or floor plan, which can be the case in grid-based scenarios such as A1 (indoor) and B1 (urban microcell), the WINNER channel model provides the option to determine the existence of NLOS/LOS propagation conditions deterministically. Table 3.11 provides median values of the large-scale parameters produced by the WINNER channel model for various scenarios.

Table 3.10 Line-of-sight probabilities. Scenario A1 B1 C1 C2 D1

LOS probability as a function of distance d (m) 1, d ≤ 2.5 3 1/3 PL O S = , d > 2.5 1 − 0.9 1 − 1.24 − 0.61 log10 (d) PLOS = min(18/d, 1) · (1 − exp(−d/36)) + exp(−d/36) d PLOS = exp − 200 PLOS = min(18/d, 1) · (1 − exp(−d/63)) + exp(−d/63) d PLOS = exp − 1000


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Table 3.11 Median output values of large-scale parameters: delay spread (DS), angular spread (AS), and elevation spread (ES). Scenario A1 B4 B1 C1 C2 D1 D2

LOS NLOS NLOS LOS NLOS LOS NLOS LOS NLOS LOS NLOS LOS

DS (ns)

AS at BS (◦ )

AS at MS (◦ )

ES at BS (◦ )

ES at MS (◦ )

40 25 49 36 76 59 75 41 234 16 37 39

44 53 18 3 15 6 8 10 8 6 9 5

45 49 58 25 35 30 45 50 53 16 33 32

8 11 10

9 13 10

3.5 Channel Model Usage The purpose of this section is to discuss issues concerning usage of the WINNER channel model for simulations.

3.5.1 System-level Description 3.5.1.1 Coordinate System Figure 3.21 shows a system layout in the Cartesian coordinate system. As shown in Table 3.12, all the BS and MS have (x,y) coordinates. MS and cells (sectors) also have array broadside orientation, where north (up) is the zero angle. The angles are positive in the clockwise direction (see Figure 3.22). Both the distance and line of sight (LOS) direction information of the radio links are calculated for the input of the model. A pairing matrix A is used to contain the information of simulated links. A link from celln to MSm that is not modelled has a value of 0 and a link that is modelled has a value of 1. The pairing matrix can be applied to select which radio links are generated.

3.5.1.2 Single User (Handover) Multicell Simulation A handover situation is characterised by a MS moving from the coverage area of one BS to the coverage area of another BS (see Figure 3.23). There are two base-stations or cells denoted c1 and c2, and one mobile station; each location of the mobile station on its path is assigned a unique label ms1 to msM. This is equivalent to a scenario with multiple mobile stations at different positions ms1 to msM. Path-loss is determined according to the geometry and large-scaley parameters correlate properly. The

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Y

cell3

cell1 MS1

cell2

MS2 BS1

cell4 cell6

MS3

cell5

BS2 x

Figure 3.21 System layout of multiple base stations and mobile stations.

resulting procedure is as follows:

1. Set base station locations c1 and c2 and array orientations according to geometry. 2. Set MS locations ms1 to msM and array orientations along the route. Choose the distance between adjacent locations according to the desired accuracy. 3. Set all the entries of the pairing matrix to 1. 4. Generate all the radio links at once to obtain the correct correlation properties. It is possible to generate more channel realisations, i.e. time samples, later for each channel segment. This can be done by applying the same values of small-scale parameters and restoring final phases of the rays. 5. Simulate channel segments consecutively to emulate motion along the route.

Table 3.12 Transceiver coordinates and orientations. Tranceiver BS1

BS2

MS1 MS2 MS3

cell1 cell2 cell3 cell4 cell5 cell6

Coordinates

Orientation (◦ )

(xbs1 ,ybs1 ) (xbs1 ,ybs1 ) (xbs1 ,ybs1 ) (xbs2 ,ybs2 ) (xbs2 ,ybs2 ) (xbs2 ,ybs2 ) (xms1 ,yms1 ) (xms2 ,yms2 ) (xms3 ,yms3 )

c1 c2 c3 c4 c5 c6 ms1 ms2 ms3


83

N

BS array ΩBS

t

N qBSt , MSt

MS array

BS array broadside ΩMSt qMSt , BSt BS array broadside

Figure 3.22 BS and MS antenna array orientations.

It is also possible to model even more accurate time evolution between locations as described in Section 3.4. The clusters of the current channel segment (location) are replaced by clusters of the next channel segment one by one. Other cases of such a single-user multicell setup are found in the context of multiple-BS protocols, where a MS receives data from multiple BS simultaneously.

Figure 3.23 Handover scenario.

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Figure 3.24 A drive-by scenario (with multiple mobile stations).

3.5.1.3 Multi-user Multicell Simulation The extension of the single-user setup to multiple users (and one or more base stations) is straightforward. Because a location and a mobile station index are treated equivalently, it follows that all locations of all mobiles have to be defined. Consider the drive-by situation in Figure 3.24. Here, M locations of mobile station 1 and N locations of mobile station 2 are defined yielding a total of M+N points or labels. The resulting procedure is as follows. 1. 2. 3. 4.

Set BS locations c1 and c2 and array orientations according to layout. Set MS locations ms11 to ms2N and array orientations according to layout. Set the links to be modelled to 1 in the pairing matrix. Generate all the radio links at once to obtain the correct correlation properties. It is possible to generate more channel realisations, i.e. time samples, later for each channel segment. This can be done by applying the same values of small-scale parameters and restoring final phases of the rays. 5. Simulate channel segments in parallel or consecutively according to the desired motion of the mobiles.

3.5.2 SPACE–TIME Concept in Simulations In the end, the channel-sampling frequency has to be equal to the simulation-system sampling frequency. For the computational complexity to be feasible it is not possible to generate channel realisations on the sampling frequency of the system to be simulated. The channel realisations have to be generated on some lower sampling frequency and then interpolated to the desired frequency. A practical solution is e.g. to generate channel samples with sample density (over-sampling factor) of two, interpolate them accurately to sample density of 64 and apply zero-order hold interpolation to the system-sampling frequency. Channel impulse responses can be generated during the simulation or stored on a file before the simulation on low sample density. Interpolation can be done during the system simulation.


85

To be able to obtain deep fades in NLOS scenarios, we suggest using 128 samples per wavelength. When obtaining channel parameters, quasi-stationarity has been assumed within intervals of 10–50 wavelengths. Therefore we propose to set the drop duration corresponding to the movement of up to 50 wavelengths.

3.5.3 Bandwidth and Frequency Dependence 3.5.3.1 Frequency Sampling The WINNER system is based on the OFDM access scheme. For simulations of the system, channel realisations in the time-frequency domain are needed. The output of a WINNER model is a channel in the time-delay domain. The time-frequency channel at any frequency can be obtained by applying the following two steps: 1. Define a vector of frequencies where the channel should be calculated. 2. Calculate the channel at defined frequencies using the Fourier transform. 3.5.3.2 Bandwidth Downscaling in the Delay Domain The channel models are delivered for the 100 MHz RF bandwidth. Some simulations may need smaller bandwidths. In describing how the downscaling should be performed, we assume that the channel parameters remain constant. There is a need for downscaling if the minimum delay sample spacing in the channel impulse response (CIR) is longer than 5 ns in the simulation. Five nanoseconds is the default minimum spacing for the channel model samples (taps) and defines thus the delay grid for the CIR taps. For all larger spacings, the model should be downscaled. The most precise way would be filtering by, e.g., a FIR filter. This would, however, create new taps in the CIR and this is not desirable. The preferred method in the delay domain is to move the original samples to the nearest location in the downsampled delay grid. In some cases, there are two such locations and the tap should be placed in the one that has the smaller delay. Sometimes two taps are located in the same delay position; they should be summed as complex numbers. 3.5.3.3 Bandwidth Downscaling in the Frequency Domain If desired, the downscaling can also be performed in the frequency domain. The original CIR specified in the delay domain is transformed into the frequency domain. The transformed CIR can be filtered, e.g. by removing the extra frequency samples or by resampling the frequency response. The maximum frequency sampling interval is determined by the coherence bandwidth: Bc =

1 Cστ

(3.18)

where σ τ is the rms delay spread and C is a scaling constant related to fading distribution. A typical value for C would be 2π for an arbitrary PDP [SMF05].

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3.5.3.4 FDD Modelling The following steps explain how to obtain both the uplink and downlink channel of an FDD system with bandwidths of 100 MHz. The centre carrier frequencies are fc and fc + fc . 1. 2. 3. 4. 5.

Define BS and MS positions. Calculate the channel for one link, e.g. BS to MS at a certain carrier frequency f c . Save the small-scale parameters. Exchange the positions of the BS and MS. Calculate the other link, in this example the MS to BS link by: r using saved small-scale parameters; r randomising the initial phases of rays; r changing the carrier frequency to f c + f .

3.5.4 Approximation of Channel Models The WINNER generic model is designed to apply to many different simulations and to cover a high number of scenarios with several combinations of large-scale and small-scale parameters. The generic model is the most accurate model and is recommended for use whenever possible. However, the channel model can be simplified (approximated) to reduce simulation complexity. It has to be done very carefully. When approximating the model, reality is reduced, and the impact of the approximation has to be understood. The impact depends on, e.g., the transceiver system, algorithms, modulation, coding, multi-antenna technology, and required accuracy of the simulation results. If you are uncertain whether the approximation affects the simulation results, it is better not to approximate. The following approximation steps should only be carried out by simulation experts. Firstly, we can approximate the model by assuming no correlation between large-scale parameters (angular spreads, delay spread, and shadowing). Secondly, all the large-scale parameters can be fixed to median values. Furthermore, we can reduce complexity of the model by fixing the delays, but keeping the angles as random. The fourth approximation can be done by freezing all propagation parameters to obtain the clustered delay line (CDL) model. If a correlation model is desired, the correlation matrices can be calculated from the CDL model by fixing the antenna structure. One set of possible approximations is shown below:

r generic model [WIN2D112]; r uncorrelated large-scale parameters; r fixed large-scale parameters; r constant delays, random angles (CDL with random angles); r CDL model (see Section 3.5.4.1). 3.5.4.1 Reduced Complexity Models A need has been identified for reduced-complexity channel models that can be used in rapid simulations having the objective of making comparisons between system alternatives at linklevel (e.g. modulation and coding choices). In this book, such models are referred to as reducedcomplexity models and have the character of the well-known tapped delay line class of fading


87

channel models. Typical input to traditional tapped delay line models is the specification of relative powers, envelope-fading distributions, and fading rates. However more detailed information is required to address the needs of MIMO channel modelling. Specifically, multipath AoD and AoA information is inherent in the determination of tap fading characteristics. For these reasons, the reduced-complexity models reported here are referred to as cluster delay line (CDL) models. A cluster is centred at each tap. In general, each cluster consists of the vector sum of equal-powered MPCs (sinusoids), all of which have the same or similar delay and angles of arrival and departure. Each MPC has a varying phase, but has fixed AoA and AoD offsets. The angular offset depends on the angular spreads at the MS and the BS. The offset values were chosen to realise a specified Laplacian power azimuth spectrum (PAS) for each cluster, appropriate to the scenario being modelled. In cases where there is a desire to simulate Ricean-like fading, an extra MPC is added and given a power appropriate to the desired Rice factor and zero angular offset. The powers and delays of the clusters can be non-uniform, and can be chosen to realise the desired overall channel rms delay spread. Parameters of all CDL models reflect the expected values of those used in the more complex models described in other sections of this chapter. Doppler information is not specified explicitly for CDL models. This is because Doppler is determined by the AoAs of the MPCs, MS speed and direction, and the specified antenna patterns at the MS and BS, upon which there are no restrictions, except in fixed-feeder link scenarios. Although AoA and AoD values are fixed, it is recommended to have directional variation for e.g. beamforming simulations by adding network-layout-related angle parameters MS and BS to all tabulated angles (see Figure 3.22). In the CDL model, each cluster is composed of 20 rays with fixed offset angles and identical power. In the case of a cluster where a ray of dominant power exists, the cluster has 20+1 rays. This dominant ray has a zero angle offset. The departure and arrival rays are coupled randomly. The CDL table of all scenarios of interest are given in [WIN2D112]. The CDL models offer well-defined radio channels with fixed parameters to obtain comparable simulation results with relatively low-complexity channel models.

3.5.4.2 Comparison of Complexity of Modelling Methods Computational complexity of channel models is an important issue in system performance evaluation. A complexity comparison of the WINNER modelling approach with the popular correlation-matrix-based method is studied in [KJ07]. A common supposition is that the correlation method is simpler and computationally more effective than the geometric method. The conclusion of [KJ07] is that the complexity of both methods is about the same order of magnitude. With a high number (>16) MIMO antenna pairs, the correlation-based method is clearly more complex. The computation complexity is compared in terms of the number of ‘real operations’. With the term ‘real operations’ is equated the complexity of real multiplication, division, addition and table lookup. Figure 3.25a depicts the number of real operations per delay tap per MIMO channel time sample (matrix impulse response), with different M×N MIMO antenna numbers, assuming S = 10 or 20 rays (M in Equation (3.4)) and 8th order IIR filter in the correlation matrix method. It was also noted that the complexity of channel realisation generation (the bottom block in Figure 3.18) is several order of magnitudes lower than the computational

88


Complexity of MIMO channel generation, MxN MIMO

4

5

x 10

Correlation matrix method Sum of rays S=10 Sum of rays S=20

4.5

# real operations per delay tap

4 3.5 3 2.5 2

4x4 MIMO

1.5 1 0.5 0 0

10

20

30 40 # antenna pairs (MN)

50

60

70

(a)

Complexity of channel generation and channel convolution

12

10

11

channel convolution channel generation, geom. channel generation, corr.

10

10

B = 20 MHz

# real operations

10

9

10

f

max

= 500 Hz

8

10

7

10

6

10

0

10

20

30

40 50 60 70 B [MHz] and fmax [10Hz]

80

90

100

(b)

Figure 3.25 (a) Computational complexity comparison (b) Channel convolution complexity (from [KJ07]). (Reproduced by Permission of IEEE © 2009).


89

complexity of the simulation of channel convolution (i.e. convolution of transmitted signal and channel impulse response). The computational complexity of channel simulation (i.e. convolution) is a linear function of simulated system bandwidth (see Figure 3.25b). For wideband, beyond-3G systems, the computational load of channel simulation is predominant, thus the method applied in channel coefficient generation has only marginal effect on the overall complexity. The channel simulation complexity is independent of the compared methods.

3.6 Conclusion During the two phases of the WINNER project, a comprehensive set of channel models was developed for 12 different propagation scenarios from indoor hotspot to metropolitan and wide-area scenarios. All models are based on one generic modelling principle, namely the geometry-based stochastic channel model. This means that all models can be obtained by changing only the model parameters to correspond to the propagation scenario. The parameters are obtained from measurements performed during the project and from other measurements presented in the literature. The generic model is complete in the sense that it describes the behaviour of all relevant phenomena in radio propagation: path-loss, shadowing, fast fading, temporal and spatial dispersion of propagation, cross-polarisation and various correlation properties of the propagation parameters. Due to its geometric nature, the modelling principle takes implicit care of the Doppler phenomenon that is caused by the movement of the terminal. The geometry-based modelling approach was selected because of some clear benefits compared to other approaches (such as the correlation-matrix-based approach): the geometry-based model is antenna independent – different antenna arrays and even different antenna radiation patterns can be covered with a single model; the geometry-based model utilises fully the propagation information. The WINNER channel model follows the same principles as some existing channel models, e.g., 3GPP/3GPP2 SCM. The strengths of the WINNER channel model are the following:

r parameters for large number of propagation scenarios; r support of arbitrary multi-antenna array; r variable large-scale parameters; r wide bandwidth. These characteristics make the WINNER channel models sufficient to be used in planning of future radio systems for very different environments, scalable RF bandwidths and different radio frequencies. In this respect, the WINNER model is unique. The channel models described in this book give a realistic picture of the principles of the model. The description does not cover the entire model with all the propagation scenarios and modelling details. Readers who would like to use the model are recommended to study the WINNER II deliverable [WIN2D112].

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Acknowledgements Authors would like to give thanks for the contributions of their colleagues in WINNER I and II, in particular, Mikko Alatossava, Daniel Baum, Robert Bultitude, Veli-Matti Holappa, Aihua Hong, Yvo de Jong, Marko Milojević, Milan Narandˇzić, Terhi Rautiainen, Hassan ElSallabi, Christian Schneider, Reiner Thomä, Pertti Vainikainen, Juha Ylitalo, Per Zetterberg and Xiongwen Zhao.

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[RJK07]


Rautiainen, T., Juntunen, J. and Kalliola, K. (2007) ‘Propagation analysis at 5.3 GHz in typical and bad urban macrocellular environments’, Proc. IEEE 65th Vehicular Technology Conference (VTC), Dublin, April. [RKJ05] Rautiainen, T., Kalliola, K., and Juntunen, J. (2005) ‘Wideband radio propagation characteristics at 5.3 GHz in suburban environments’, Proc. PIMRC Berlin, 2: 868–72. [RMB+06] Riback, M., Medbo, J., Berg, J.-E., Harrysson, F. and Asplund, H. (2006) ‘Carrier Frequency Effects on Path Loss’, Proc. IEEE VTC 2006 Spring, 6: 2717–21. [Rudd03] Rudd, R.F. (2003) ‘Building penetration loss for slant-paths at L-, S- and C-band.’, Proc. ICAP 2003. [S07] Suikkanen, E. (2007) ‘Wideband Radio Channel Measurements and Modelling of an Outdoor-to-Indoo Propagation Scenario at 5.25 GHz’, Master’s Thesis, University of Oulu, Finland. [Sau99] Saunders, S. (1999) Antenna and propagation for communication systems concept and design, John Wiley & Sons, Ltd, Chichester. [SMB01] Steinbauer, M., Molisch, A.F. and Bonek, E. (2001) ‘The double-directional radio channel’, IEEE Antennas and Propagation Mag., August, pp. 51–63. [SMF05] Sorensen, T.B., Mogensen, P.E., and Frederiksen, F. (2005) ‘Extension of the ITU channel models for wideband (OFDM) systems’, Proc. IEEE VTC2005-Fall, 1: 392–6, Dallas, USA. [SMI+02] Sakawa, K., Masui, H., Ishii, M., Shimizu, H. and Kobayashi, T. (2002) ‘Microwave path-loss characteristics in an urban area with base station antenna on top of a tall building’, Proc. Int. Zurich Seminar on Broadband Communications, pp. 31-1–31-4. [SV87] Saleh, A. and Valenzuela, R.A. (1987) ‘A statistical model for indoor multipath propagation’, IEEE J. Select. Areas Commun., SAC-5(2): 128–37. [USM95] Urie, A., Streeton, M. and Mourot, C. (1995) ‘An advanced TDMA Mobile Access System for UMTS’, IEEE Personal Communications Magazine, 2(1): 38–47. [WIN08] WINNER+ (2008) WINNER 1: Channel Model Implementations, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN1D54] WINNER I (2005) Final Report on Link Level and System Level Channel Models, Deliverable D5.4, November 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN1D72] WINNER I (2004) System Assessment Criteria Specification, Deliverable D7.2, v1.0, July 2004, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D112] WINNER II (2007) WINNER II Channel Models, Part I Channel Models, Deliverable D1.1.2, v1.2, February 2008, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WL02] Weitzen, J. and Lowe, T. J. (2002) ‘Measurement of angular and distance correlation properties of log-normal shadowing at 1900 MHz and its application to design of PCS systems’, IEEE Transactions on Vehicular Technology, 51(2). [YIT06] Yonezawa, I., Ishikawa, H. and Takeuchi, Y. (2006) ‘Frequency range extension of path loss prediction formula for over-rooftops propagation in microwave band’, Proc. IEEE International Symp. Antennas Propagation, pp. 4747–50. [ZHM+07] Zhao, X., Hentilä, L., Meinilä, J., Jamsa, T., Kyösti, P. and Nuutinen, J.-P. (2007) ‘Correlations of wideband channel parameters in street canyon at 2.45 and 5.25 GHz’, IEEE Antennas Wireless Propagat. Letters, 6: 252–4. [ZKH+06] Zhao, X., Kyösti, P., Hentilä, L., Jämsä, T., Meinilä, J., Laselva, D. and Nuutinen, J.-P. (2006) ‘Indoor, rural and suburban channel models and parameters for B3G link and system level simulations’, Proc. IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland. [ZMH+07] Zhao, X., Meinilä, J., Hentilä, L., Jämsä, T., Kyösti, P. and Nuutinen, J.-P. (2007) ‘Effects of noise cut for extraction of wideband channel parameters’, Proc. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2007, Athens, Greece.

4 System Concept and Architecture Martin Döttling,1 Mikael Sternad,2 Johan Nyström,3 Niklas Johanson,3 and Jörn von Häfen1 1

Nokia Siemens Networks Uppsala University 3 Ericsson 2

4.1 Introduction This chapter reviews important design principles and uses a top-down approach to describe the WINNER Radio Access Network (WRAN) based on logical nodes and interfaces. Furthermore, the framework for the protocol and service architecture necessary for interoperability is described. Together, they represent the WINNER II system concept. An important requirement for communication systems and networks is interoperability between different vendors’ equipment. The main advantage of interoperability is that resources are exploited in an efficient way and economies of scale can prevail. This is of benefit to subscribers, service providers, manufacturers, and to the entire economy and society as a whole. This chapter describes the two most important characteristics of a system concept that must be standardised to enable interoperability: the logical node architecture (Section 4.3) and the protocol architecture (Section 4.4). We begin in Section 4.2 by reviewing the design principles and desirable characteristics of the WRAN that have influenced the resulting system concept. Numerous aspects and results of the WINNER projects are important for the performance and flexibility of the concept, but they would not necessarily have to be standardised from an interoperability point of view. The collection of algorithm proposals and best design guidelines that represent the WINNER II reference design is outlined in the other chapters. The logical node architecture is a framework that describes functions, groups them as logical nodes and connects them by well-defined, open interfaces. Logical nodes are therefore defined as the smallest entities in the radio access network (including user terminals) for which interoperable interfaces need to be defined that are independent of a specific vendor. The logical node architecture is partly inspired by the 3GPP LTE/SAE architecture, but also Radio Technologies and Concepts for IMT-Advanced Martin Döttling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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contains many novel features such as a server for spectrum management and optional relaying functions. The protocol architecture describes the interfaces between logical nodes. It has a layered structure, but interaction between layers plays a crucial role in increasing the efficiency of the wireless packet data transfer. The scheduler function outlined in Section 4.4.3.3 plays a central role.

4.2 Design Principles and Main Characteristics In order to meet the requirements detailed in [PDH+06; WIN2D6114], the WINNER radio access network has been designed as a packet-oriented, user-centric, always-best concept. An always-best solution that provides competitive performance in a wide variety of situations is a challenging design goal. Different parameterisations are used to provide flexibility and maximum efficiency depending on the particular radio environment, usage scenario, economic model, etc. The always-best solution is further supported by the flexible protocol architecture of the WINNER radio interface and incorporates mechanisms for both long-term and shortterm adaptation. Both relaying and advanced spatial processing are integrated elements of the system architecture. The always-best solution is enabled by several innovative key components, including:

r A flexible logical node architecture that uses as few logical nodes as possible to keep the number of interfaces small. This is important for reducing cost and simplifying the number of interfaces that have to be taken into account by different vendors. The function grouping is defined such that it enables and encourages flat, flexible, scalable and cost-efficient physical node implementations. The logical nodes are defined in such a way that physical RAN implementations can efficiently use them as pooled resources (to avoid a single point of failure, for load-balancing, and trunking-gain purposes). r A flexible protocol architecture enabling efficient interworking between different system parameterisations is outlined in Section 4.4. The architecture focuses on the three lowest layers of the OSI stack. The two lowest layers, represented by the physical (PHY), medium access control (MAC) and radio link control (RLC) sublayers, are assumed to be present in all base stations (BS), user terminals (UT) and relay logical nodes (RN). The assumed co-location enables an efficient co-design of these layers and supports low latencies over the air interface. r Relay-enhanced cells are an integral part of the concept. A deployment can utilise advanced decode-and-forward layer-2 relay nodes, whenever this is deemed cost efficient. Such relay nodes can be used to optimise the deployment, reduce the cost, extend the range of transmission, cover shadowed areas, and re-distribute the offered capacity between cell centres and borders. Relay-enhanced cells are discussed in detail in Chapter 8 and in [WIN2D351; WIN2D352; WIN2D353]. r Design and support for operation in a shared spectrum and inter-system coordination. System functionalities that support shared spectrum use and inter-system coordination are presented in Chapters 10 and 11 of this book and in [WDK+08; WIN2D481]. r Highly optimised user-plane processing. In-band, packet control signalling is optimised across layers to provide a small overhead by means of segmentation and concatenation at RLC and multiplexing at MAC providing one single transport block per frame per UT. The

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cross-layer design of hybrid automatic repeat request (H-ARQ) and RLC-ARQ minimises overhead and status reporting. For both cases, multi-hop operation is supported efficiently. r MAC designed for short radio interface delays. A low-latency, over-the-air interface is desirable for several reasons: it enables adaptivity with respect to fast channel variations; it facilitates high throughput for TCP/IP traffic and low-latency services; and it enables fast link retransmission, which is an advantage for perceived performance at higher layers. The system concept provides a short-delay, over-the-air interface: 1 ms in downlinks and 2 ms in uplinks over single hops. It achieves this by a combination of short frame durations and tight feedback-control loops. r Advanced interference-control mechanisms. Multi-antenna transmission and reception is integrated into the WINNER system concept (see Chapter 7). It offers versatile tools for suppressing interference at receivers [WIN2D473] and intelligent transmission processing [WIN2D472]. The WINNER concept also enables the use of additional mechanisms such as resource allocation that targets interference avoidance by coordinated scheduling across base stations and relay nodes or the use of (spatial) precoding over distributed antennas. r An optimised physical layer design. The WINNER physical layer concept is based on generalised multi-carrier transmission. This technique is based on cyclic-prefix orthogonal frequency division multiplexing (OFDM) in combination with extra discrete Fourier transform (DFT) precoding steps. Both single-carrier and multi-carrier waveforms can be generated and received within one transmitter/receiver chain with low computational complexity [WIN1D210]. The basic resource elements assumed in the WINNER concept are outlined in Section 4.4.3.3. The general transmission technique can be applied in different configurations to ensure low complexity, high spectral efficiency, and high granularity of resource elements. The physical layer processing is described in detail in Chapters 5, 6, and 7 as well as in [WIN2D223; WIN2D233; WIN2D341]. r A spatial multi-user link adaptation concept that can be applied to a wide range of deployments, operational scenarios, propagation channels, service requirements, and terminal capabilities. The core elements are: r A scheduler that uses tight inter-layer interaction between the RLC, MAC, and PHY layers to accomplish joint link adaptation and channel-aware scheduling (see Chapter 9). r Two resource allocation principles (see Chapter 9): frequency-adaptive transmission uses individual link adaptation within rectangular time–frequency units (chunks), supported by efficient control signalling; Non-frequency-adaptive transmission uses smaller time–frequency blocks for diversity-based transmission. Both use the advanced channel coding schemes outlined in Chapter 5 (complete descriptions are available in [WIN2D223]). r Control signalling that supports adaptive transmission with reasonable overheads, achieved by a combination of efficient source and channel-coding techniques, as well as an adaptive control channel format to support usage scenarios from large wide-area cells to small local-area hotspots [SSD08; WIN2D61314]. r A generic multi-antenna transmit/receive scheme, which can be configured into various diversity, multiplexing and multi-user MIMO configurations (Chapter 7). The spatial transmission can be adjusted individually to the needs of different packet flows to and from a user terminal. r Pilot schemes that support various types of adaptive transmission and multi-antenna transmission with acceptable overheads (Chapter 6).

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4.3 Logical Node Architecture The WINNER system is designed to work well over a wide range of operating scenarios. The logical node architecture represents a high-level functional abstraction of all of these designs and shows the basic flow of user data, control data, and functional interactions without having to consider specific physical design implementations or scenario-dependent parameter settings. The logical node architecture is a framework for the description of the WINNER system concept that identifies functions, groups them into logical nodes and connects the nodes by well-defined, open interfaces. Logical nodes are defined as the smallest entities in the radio access network, including the user terminal, for which interoperable interfaces are defined that are independent of the implementation of a specific vendor. This enables operators to buy interoperable equipment from multiple vendors. Physical nodes are particular implementations, physical devices that may contain multiple logical nodes depending on the specific deployment scenario. The logical node architecture also offers a compact, top-down overview of the WINNER system starting from an abstract level and refining down to detailed protocol definitions.

4.3.1 Overview The WINNER Radio Access Network (WRAN) is connected to an external packet data network (e.g. the Internet) via the IG interface (see Figure 4.1). The WRAN provides the IWU radio interface to connect WINNER terminals (‘UT’). Strictly speaking, the WRAN also contains functionalities, such as gateway functionalities, that are more related to the core network than the radio network. Nevertheless, for simplicity we will use the term WRAN to denote the access network. In order to accommodate handover and other cooperation functions (e.g., spectrum sharing) between WRANs and non-WINNER access networks, WINNER has to comply with a cooperation architecture provided outside the WINNER RAN. Thus, each of the WRANs has to support its respective functionalities, e.g. to provide support functions such as measurements. As is shown in Figure 4.2, the WINNER logical node architecture [WDK+08] consists of the base station logical node, BSLN , the relay node logical node, RNLN , the user terminal logical node, UTLN , the SpectrumServerLN and RRMServerLN logical nodes and two types of gateway logical node: the IP anchor, GW IPALN , and the control, GW CLN .

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Figure 4.1 Overview of the WINNER architecture.


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Figure 4.2 WINNER logical nodes and interfaces.

The logical nodes are connected by logical interfaces denoted as ‘I’. Protocols are defined by the termination points and the interfaces between them. For a detailed description of the interfaces, the reader is referred to [WIN2D61314]. The GW IPALN provides access to external data networks (e.g., the Internet, corporate networks, or operator-controlled core networks) and operator services (e.g., Multimedia Broadcast Multicast Service (MBMS)). It also terminates flows on the network side and serves as the anchor point for external routing. Thus, all functions that operate on user data traffic are located here. It is accompanied by the GW CLN , which provides control functions for UTLN s that are not active (i.e., terminals that are in idle mode) and functions that control and configure the GW IPALN . In practice, there will most likely be several GW IPALN s present in the network as well as an independent number of GW CLN s. The WRAN contains two types of radio access point (RAP): BSLN and RNLN . The RNLN is a logical network node with relaying capabilities that is wirelessly connected to a BSLN or another RNLN . The RNLN communicates with the BSLN using the IBRN interface. A WINNER terminal, UTLN , is connected to the WRAN via the IWU interface and communicates either directly with the BSLN or indirectly to the BSLN via the RNLN . The IWU interface is exactly the same whether the connecting node is a base station or a relay node. The BSLN performs all radio-related functions for active terminals (i.e., terminals sending data) and is responsible for governing radio transmission to and reception from UTLN s and RNLN s in one cell. The BSLN also controls the resources the RNLN uses. The BSLN s are connected to each other by the IBB interface. The BSLN is connected to the GW IPALN and GW CLN pair via the IGPB and IGPC interfaces, respectively. These interfaces are multinode-to-multinode-capable interfaces, meaning that one BSLN can be connected to multiple GW IPALN and GW CLN pairs and, conversely, that one GW IPALN and GW CLN pair can be connected to multiple BSLN s. The GW IPALN and GW CLN form a pool of equipment that may cover large areas, e.g. cities. A UTLN is said to be associated with a GW IPALN and GW CLN pair when the GW IPALN provides an anchor point for the traffic to and from the UTLN and the GW CLN controls

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functionality for the UTLN . Two UTLN s connected to the same BSLN can be associated with different GW IPALN s and GW CLN s. Conversely, two UTLN s associated with the same GW IPALN and GW CLN pair can be connected to different BSLN s. The SpectrumServerLN is a logical node that contains centralised functions related to spectrum sharing and spectrum assignment [WDK+08]. It enables sharing and co-existence with other radio access technologies and spectrum assignment between WRANs. The SpectrumServerLN interfaces with the GW CLN to obtain information about associated UTLN s and their characteristics. The SpectrumServerLN communicates spectrum-sharing and spectrum-assignment decisions to the BSLN . The SpectrumServerLN is accessible via the GW IPALN for spectrum negotiations. The RRMserverLN is a logical node that contains centralised functions related to radioresource management.

4.3.2 Pool Concept and Micro Mobility The GWLN pool concept decouples the physical relation between a GWLN and a number of BSLN s in a pool area. The pool area is defined as an area in which a UTLN may roam without needing to change GWLN . Each GWLN is connected to each BSLN in the pool area. As is shown in Figure 4.3, there is at any moment an association between a UTLN and an IP anchor, GW IPALN , and between the UTLN and a control, GW CLN . The association between a GW IPALN and a GW CLN for one UTLN may be different from that of another UTLN connected to the same BSLN . For example, two UTLN s may be associated with the same GW CLN but different GW IPALN s. One advantage of defining these two types of gateways is improved dimensioning and scalability of the gateway functionalities according to the need in the deployed network. Furthermore, it gives vendors more freedom to build e.g. a physical base station product with the user-plane gateway functionalities close to the BSLN , while having a more centrally located

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Figure 4.3 One set of GW CLN may serve a (larger) set of GW IPALN . Each UTLN is associated with one GW CLN –GW IPALN pair (from [WDK+09]). (Reproduced by Permission of IEEE © 2009).


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control server. A drawback with the division of gateway functionalities in this way is that there is a need for a new standardised interface, IGPC. There is also an interface ICC between multiple instances of GW CLN for relocation and information transfer between two GW CLN . If the GW IPALN is relocated, or if two BSLN s are simultaneously relocated (a rare event since we have overlapping pool areas) and lack an IBB interface (also a rare event), packet forwarding between these two BSLN s is performed by the two GW IPALN s. Hence there is no need for an interface between the two GW IPALN s since no control signalling is performed between them. For simplicity and visual clarity in the figures, when this split is not of essence for the discussion, the GW IPALN –GW CLN pair is represented by one logical node, the GWLN . In addition, the interfaces IGPB and IGPC , can be represented by the interface IGB. In most of this document, we use this simplified view (see Figure 4.4). The logical association between a UTLN and the GWLN is independent of the BSLN s and can be kept during a handover (see Figure 4.5). Hence, the set of GWLN can be seen as a pool of resources. Within a pool, each BSLN can forward user traffic to or from any GWLN ; conversely, each GWLN can communicate with all BSLN s. Of course, the UTLN –GWLN association can change if necessary, e.g. for load-balancing purposes. This also facilitates network scalability. The GWLN pool concept decouples the physical relation between a GWLN and a number of BSLN s in a pool area. Instead, each GWLN is connected to each BSLN in the pool area. From that, the pool area is defined as an area in which a UTLN may roam without the need to change GWLN . The GWLN capacity of a pool area can be scaled simply by adding more GWLN s (more accurately, GW IPALN s and GW CLN s can be added independently). In contrast, in a hierarchical structure, each BSLN is connected to the one GWLN that serves its location area. There are several benefits of having the set of GWLN s as a pool of resources:

r It reduces the requirements for micro mobility since the GWLN is an anchor point for external routing.

r The pool capacity is easily optimised by adding or removing GWLN s. IG GW GW_C

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Figure 4.4 Simplified representation of the GW IPALN and GW CLN as GWLN .

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Figure 4.5 The UTLN –GWLN association is normally kept during handover.

r Single points of failure are avoided: should a GWLN fail, the users can be handed over to any other GWLN .

r Load balancing can easily be achieved, e.g., to support services such as MBMS. The pool concept includes logical associations from each GWLN to each BSLN , which means that fully meshed interconnections (i.e. a switching or routing function) are needed (see Figure 4.6). For the routing, L3 technologies such as IP may be feasible provided they implement a virtual logical connection from each GWLN to each BSLN that allows micro mobility without reconfiguration of routing tables or change of IP addresses. An IP-based

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tunnelling protocol that provides tunnels between GWLN s and BSs is envisaged. The GWLN routes any data for a specific BSLN by addressing the corresponding tunnel. The aim for a large pool area may lead to a situation where a UTLN is associated with a GWLN that is located very far away from the BSLN it is connected to. This may increase the signalling load and memory requirements compared to a hierarchical structure in which the signalling is kept locally in the location area of a GWLN . However, each vendor may provide intelligent functions for optimising these associations with respect to delay and signalling load.

4.3.3 Equipment Sharing It is an important requirement that multiple operators can share part of the same physical RAN, for example, the transport network, and the physical BSs and RNs. This is supported by multiple sets of GWLN s, each connecting to different operator backbone networks. Each UTLN can then associate with the GWLN relevant for the operator in question (see Figure 4.7). The operators will thus be able to share the physical BSs and RNs, as well as the transport network (not depicted) connecting the BSs. The operators must have different sets of GWLN but nothing prevents a vendor from manufacturing physical GWs that can host multiple logical GWLN s belonging to different operators, thus enabling operators to share physical GWs.


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Figure 4.7 Equipment-sharing support in the WINNER logical node architecture.

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4.3.4 Multicast and Broadcast Service Support Support for multicast and broadcast service (MBMS) has been identified as an important requirement for WINNER. While unicast traffic can be handled efficiently with a one-to-one association from a GW IPALN to a UTLN , the situation for MBMS is different. An efficient implementation of an MBMS service includes a central entity that multicasts the traffic to all base stations with connected UTLN s that are using this service. In a hierarchical concept, that is obviously simple because the MBMS traffic only has to be distributed to the GW IPALN s to which the UTLN s are connected. Forwarding within the location area is handled by the appropriate GW CLN and the signalling traffic is kept on the transport network that is logically behind this GW IPALN . UTLN s moving between location areas may require only an update of the routing to the GW IPALN s. In the pool concept, one GW IPALN has to take the role of a central anchor point that distributes the MBMS traffic to all UTLN s within the pool area that subscribe to this service. They may, however, be associated with different GW IPALN s for their unicast traffic. In addition, multiple MBMS sessions may be ongoing in parallel to many (partly overlapping) UTLN groups. In order to support MBMS, a UTLN is required to associate with multiple GW IPALN s. In this case, it is assumed that unicast traffic of a UTLN is still terminated by a single GW IPALN so that the one-to-one relation between the GWLN and the UTLN is kept for unicast traffic, see Figure 4.8. The multicast flows for each UTLN may however come from a different GW IPALN . In the pool concept, this means that the GW IPALN anchoring MBMS traffic has to be updated with all cell changes of UTLN s that are using this MBMS service. As the forwarding of all these UTLN s is handled by different GW IPALN s, in the general case (their unicast GWLN s), and no inter-GW IPALN interface is intended, the unicast forwarding

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Figure 4.8 Multiple associations between UTLN s and GWLN s.


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Figure 4.9 Case A: Combined unicast and multicast traffic and Case B: MBMS using single frequency network.

procedures cannot be used to update the MBMS GWLN about any cell changes of the UTLN s. Thus, an additional forwarding update procedure for the MBMS GWLN must run in parallel to the unicast forwarding procedure. Broadcast and multicast traffic network may be transmitted in a single frequency network (SFN) network by multiple BSLN s in the same frequency band so that the UTLN s are able to utilise the multiple signals, see Figure 4.9. In this case, the broadcast or multicast traffic is transmitted from the GWLN to a serving BSLN . It is the same BSLN that controls the unicast traffic to a particular UTLN . In case A, the UT is associated with one GW for unicast traffic, and another for dedicated MBMS; in case B, multiple BSs form a single frequency network: the UT is associated with one MBMS GW and receives MBMS data from multiple BSs forming an SFN. Mechanisms for coordinating simultaneous multicast transmissions from multiple base stations to pools of user terminals are required. For WRAN, it is assumed that the GW CLN controls the respective BSLN in the single frequency network area.

4.3.5 Multiband Transmission from Different BSs The logical node architecture also supports simultaneous transmissions from multiple BSLN s to one UTLN (as shown in Figure 4.10) to support overlay networks. As an example, it enables flows (e.g. VoIP) that are characterised by a low data rate but are sensitive to delay and jitter are transmitted by an overlay wide-area cell to avoid frequent handover situations while highdata-rate flows that are delay- and jitter-insensitive are transmitted by a local-area cell. The traffic is distributed by the GWLN to the BSLN s. It also provides gains in load and admission

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Figure 4.10 Multiband transmission from two physically separated BSLN to one physical UT containing two UTLN .

control. In Figure 4.10, the physical implementations of the user terminal and base station nodes are denoted by UTPN and BSPN , respectively. It should be noted that this option addresses the situation where both cells operate in different bands and the physical UT supports multiple transceivers that are modelled by different UTLN . Furthermore, the network sees a physical terminal with two logical nodes as two separate entities. Transmission from two BSLN s in the same band to the same physical UTLN is currently indicative of MBMS traffic network.

4.3.6 Logical Nodes This section provides a description of the WINNER logical nodes network and the functions that they perform. These functions are defined in more detail in subsequent chapters. 4.3.6.1 Gateway Nodes: GW IPALN and GW CLN The gateway functionalities are performed by a GW IPALN and a GW CLN . Generally, a GW IPALN is an IP anchor (IPA) for the user, which also routes the data, whereas a GW CLN controls various aspects of the data flows in the GW IPALN . Certain functionalities are represented in both logical nodes; however, different aspects are captured in the different types of node. For example, policy enforcement is performed on the data by the GW IPALN but policy enforcement is controlled by the GW CLN (i.e. GW CLN configures the GW IPALN with respect to policy enforcement). Pooling a number of GW CLN or GW IPALN nodes eliminates the risk that one node failure will cause parts of the network to be out of service. It also enables load sharing between different GW CLN or GW IPALN nodes. The pooling is possible since there is a many-tomany interface between the BSLN , GW CLN and GW IPALN nodes; each BSLN is associated


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with a set of GW CLN and GW IPALN nodes, called a GW CLN –GW IPALN pool. When a UTLN attaches to the network, it is assigned to a GW CLN and a GW IPALN in this pool. No change of GW CLN or GW IPALN is normally required while the UTLN moves around among BSLN s. The GW IPALN and GW CLN provide the interface to the outside world. The GW IPALN acts as the IP anchor point for external routing functionality. One association of GW IPALN and UTLN is generally maintained for unicast traffic as long as the UTLN is located within the pool area. The GW IPALN contains functions that perform data forwarding, if the user terminal moves. In order to ensure data integrity and confidentiality within the WRAN, header compression and ciphering are performed in the GW IPALN (configured by the GW CLN ). A flow is a packet stream from one source to one or several destinations, classified by quality of service (QoS) requirements. A flow class is identified by its attributes (guaranteed bit rates versus non-guaranteed bit rates, maximum bit rates, delay budget, loss tolerance, etc.). These attributes are configurable and reconfigurable as new flows are set up or multiplexed onto an existing flow class and are governed by filters in the BS and UT that map flows onto flow classes. The flow class setup and release procedures are performed by the GW CLN . QoS is also addressed in this node to configure access network elements (e.g. routers within the WRAN); the details are beyond the scope of this book. In addition, the GW CLN communicates with an external authentication authorisation accounting (AAA) server to handle authentication and authorisation requests by the UTLN s. Once a UTLN has been authorised, the GW CLN is responsible for forwarding charging-related information to the external AAA server. The GW CLN encourages power saving by providing support for idle mode UTLN s. The GW CLN is informed by the UTLN about the current paging area when the UTLN moves and updates an internal database with this information. It initiates the paging procedure to when the UTLN state changes. When the UTLN is in an active state, the GW IPALN forwards the data towards the UTLN (using the BSLN to which the UTLN is attached). If an active UTLN moves, the pathswitch function in the GW CLN is informed by the serving BSLN about handover decisions. The GW CLN then reconfigures the GW IPALN so that the flows are routed via the target BSLN . MBMS traffic is supported by the GW CLN and GW IPALN in a similar way, except that a MBMS flow is used instead of a unicast flow. The flow types differ with respect to the maximum number of destination UTLN s. For each MBMS session, one MBMS flow is established to all BSLN s within a single frequency network area (i.e. several BSs use coordinated transmission to obtain downlink macro diversity – for more details, see [3GPP08]) in which at least one UTLN subscribes to that session. It is up to the BSLN to select an appropriate channel to transmit the data to the UTLN given in the MBMS flow. In summary, the following functions are performed by the GW IPALN :

r forwarding of unicast and multicast/broadcast services; r header compression; r ciphering; r policy enforcement; r traffic measurements (for charging purposes).

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In addition to the control and configuration of the functions performed in the GW IPALN , the following functions reside in the GW CLN :

r idle-mode mobility control; r support for micro mobility; r authorisation and authentication; r flow class establishment and release; r flow class admission control; r QoS signalling; r integrity protection; r paging. 4.3.6.2 Base Station Node: BSLN The BSLN performs all radio-related functions for both active and idle user terminals and is responsible for governing radio transmission to and reception from UTLN s and RNLN s in one or more cells. The BSLN is in control of relays (if used); it determines routes, forwards packets to the appropriate relay and takes care of flow control for the relays to ensure that they can forward the data to their associated UTLN s. User mobility in active mode UTLN s is handled by the BSLN . It determines the necessary handovers based on measurements obtained by the UTLN and information governed by neighbouring BSLN s. The handover decision between relays is determined in a similar manner, based on measurements obtained by the UTLN to collect the link information. Furthermore, the load situation at all involved nodes and the quality of the link between a BS or an RN and an RN is taken into account. Each UTLN that is served by the BSLN is represented by a user context that is kept in the BSLN . Each flow class that is transmitted via the BSLN is represented by a flow-class context in the BSLN . In the regular case, a UTLN handover involves transfer of the user context and all UTLN flow-class contexts to the target BSLN using the IBB interface. If a UTLN is connected to only one BSLN (the regular case), all flow-class contexts and the user context are linked and kept in the serving BSLN . In the special case of a dual transceiver (physical) UT that is served by two BSLN s operating in different bands, a handover (flow-class context transfer) of individual flow classes is possible. In this case, a separate user and flow class context is kept in each BSLN for each UTLN to which the physical UT is connected. In order to detect overload situations, load supervision is located in the BSLN . To prevent overload situations, the BSLN may perform countermeasures such as load balancing and flowclass admission. The following functions reside in the BSLN :

r flow class admission control; r flow control between RAPs within a relay-enhanced cell (REC); r packet scheduling over the radio interface; r outer ARQ; r buffer management; r lower layer QoS configuration;


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r route establishment within a REC; r user-context transfer; r flow-class-context transfer; r load supervision; r load sharing and control; r forwarding; r horizontal spectrum sharing with or without coordination; r short-term spectrum assignment; r micro mobility (for BS and RN); r resource partitioning; r lower-layer functions, such as scheduling and channel coding for delay and processing. Detailed descriptions of these functions are given in subsequent chapters.

4.3.6.3 Relay Node: RNLN The RNLN is a logical network node with relaying capabilities that is wirelessly connected to a BSLN , a UTLN or another RNLN . It contains a forwarding function and schedules packets on the radio interface. Furthermore, system information broadcast, provided by the BSLN , is relayed by the RNLN . In WINNER, an RNLN is assumed to operate as a decode-and-forward L2 relay. Decode-andforward L2 relays allow advanced forwarding and can take advantage of adaptive transmission with different modulation and coding schemes on the different hops. The relaying solution is not optimised to reside in a physical mobile device, but rather in a fixed installation. Nothing in the logical node architecture prevents mobile relays, but mechanisms for mobility support have not been studied. RNLN s serves UTLN s in the same way as BSLN s, which means the UTLN does not need an extra mode or reconfiguration. The relaying concept is primarily designed and optimised for two hops (BSLN –RNLN –UTLN ) in order to achieve a high performance relay deployment. Nevertheless, the concept aims at supporting any number of hops in order to allow a high degree of deployment flexibility, e.g. in areas where coverage has priority over performance. A tree topology is used, as it is less complex than, e.g., a mesh topology. In the case of node failure, the RNLN should autonomously connect itself to another RAP in its range. Although the re-association to the network is not seamless and can lead to some lost connections, the tree topology can still be assumed to be self-healing. The following functions reside in the RNLN :

r packet scheduling over the radio interface; r flow control between RAPs; r forwarding in the REC; r outer ARQ; r horizontal spectrum-sharing mechanisms related to lower-layer access in a shared medium; r lower-layer functions, such as scheduling and channel coding for delay and processing.

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4.3.6.4 User Terminal: UTLN The UTLN comprises all functionality necessary to communicate directly with the network, i.e. a BSLN or an RNLN . It contains functions to handle UTLN mobility in active and idle states, as well as functionality to perform an initial access to the network. It also contains functionality to initiate flow establishment. The function list below contains only those functions that are important for understanding the characteristics of the interfaces within the logical node architecture:

r initial access; r paging area update; r flow class establishment. 4.3.6.5 RRMserverLN The RRMserverLN terminates only network internal protocols (i.e. it does not have a direct relation to the user terminals). The following functions are included in a RRMserverLN :

r load sharing and control; r micro mobility; r admission control; r resource partitioning. However, further study must decide if these functions can be implemented in a distributed way in the mandatory logical nodes. Thus, if an RRMserverLN is present, the central instances of these functions can take over from the distributed instances and take decisions using information from a larger part of the network. 4.3.6.6 SpectrumServerLN The SpectrumServerLN is a logical node that contains centralised spectrum sharing and functions related to spectrum assignment. The SpectrumServerLN enables sharing and coexistence with other radio access technologies and spectrum assignment between WRANs. The SpectrumServerLN interfaces with the gateway. The SpectrumServerLN monitors the load, contains constraints on the available spectrum (e.g. information about exclusion zones), and keeps track of the available spectrum from spectrum sharing and spectrum assignment. The spectrum functions in the SpectrumServerLN interact with the spectrum functions in the base stations, so that spectrum availability can be communicated and local optimisations of the spectrum allocation can be made by mechanisms residing in the BSLN . The following functions (further detailed in Chapter 11) reside in the SpectrumServerLN :

r vertical sharing; r centralised component of horizontal sharing with coordination; r spectrum register; r long-term spectrum assignment.


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Figure 4.11 WINNER Radio Access Network protocol layers.

For horizontal sharing with coordination, most functions reside in the BSLN ; the SpectrumServerLN only contains the functions related to centralised coordination of horizontal sharing.

4.4 Protocol and Service Architecture In this section, the radio protocol architecture is described for the user plane and the control plane.

4.4.1 Overview The WINNER radio protocol stack is presented in Figure 4.11. It is composed of a physical layer (layer 1), a data link layer (layer 2) with sublayers MAC, RLC and IPCL, and a network RRC layer (layer 3). The protocol architecture is subdivided into the user (U) plane, composed of protocols devoted to user data-transfer services, and the control (C) plane, composed of the protocols created to control data transfer, user and network operation. The IPCL and RRC sublayers only exist in the user plane and the control plane, respectively.

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A user plane connection (an IPCL layer session) can generate several RLC layer flows using different QoS classes. Communication between consecutive layers and sublayers is carried out through service access points (SAPs), depicted as horizontal ellipses in Figure 4.11. Communication between the RRC control layer and the other layers is carried through control SAPs (C-SAPs), shown as vertical ellipses in Figure 4.11. The lower layers offer services to the upper layers via the SAPs. WINNER considers two physical layer modes: FDD and TDD. Within each mode, different parameterisations of the WINNER system can be used in order to have a scalable and optimised system for various deployments. The SAP between the MAC and PHY layers provide the transport channels and the SAPs between the MAC and RLC layers provide the logical channels. A service data unit (SDU) is a packet serving as input to a protocol layer, whereas the output of a protocol layer is denoted as (PDU). For example, a packet in a transport channel is a PHY SDU and a MAC PDU. The mapping of protocols to logical nodes is illustrated in Figure 4.12. The user plane is shown in darker grey boxes and the control plane is shown in lighter grey boxes. Note that the RRC in the BS terminates protocols initiated at both the RN and the UT. In the two-hop case (Figure 4.12b), the user plane protocols terminate on the network side at the BS except for the IPCL protocol, which ends at the GW. The network layer C-plane signalling is handled by RRC, which terminates in the UT and the BS. The control and configuration of the RNLN is performed by the sub-layer RRC2. The non-access stratum (NAS) is a functional layer in the protocol stack that supports signalling and traffic between the core network and the user terminal. The NAS protocols end at the GW in the WINNER RAN and in the Home Subscriber Server (HSS) in the core network. The GW communicates and connects with external networks and nodes using the IP protocol. The NAS protocol is not fully covered by the scope of this book and is only mentioned for reference of RRC functions that need interaction with this protocol. This protocol layer is used in idle-mode mobility management and paging; authentication, authorisation and accounting; and on data flow establishment management and release.

4.4.2 Layer 3: Radio Resource Control The radio resource control (RRC) layer controls the radio resources and configures the user terminal accordingly. The RRC layer includes measurement, exchange and control of indicators related to radio resources and commands between the WRAN and the UTLN s. The measurements include standardised radio-resource indicators that measure or assist in estimation of the available and potential radio resources. The exchange of radio-resource-related indicators includes the procedures and primitives between logical entities used for requesting and reporting such measurements or estimations. The resulting information may be made available implicitly, within the measuring stations using proprietary procedures and primitives that are not subject to standards, or explicitly, to a remote functional entity using standardised procedures and primitives. The control mechanism refers to the decisions made by the measuring station or remote entity to adjust radio resources based on the reported measurements, other information or the radio resource management (RRM) functions and communication of the adjustments to logical entities using standardised primitives. RRM functions are explained in further detail in Chapter 10.


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Figure 4.12 WRAN user and control plane architecture and termination: (a) single hop and (b) two hops.

The control and configuration of RNLN s is carried out by introducing a new sublayer (RRC2) within the RRC layer. The signalling from RRC2 is routed through a dedicated RLC instance for the sake of reliable data transfer. RRC2 instances are available only in BSLN s and RNLN s, i.e. UTLN functionality is not involved. This is also true of RLC instances that are used by the RRC2 protocol. The basic control plane functions performed by the RRC layer are as follows:

r Broadcast of system information: Basic cell identification and cell-specific information that changes frequently are candidates for transmission by the broadcast channel. Further WINNER information elements suitable for broadcast include: spectrum-sharing restriction parameters (slow), shared-band availability, cell ID, operator ID, Tx power mask, FDD/TDD duplex mode information, a pointer to the next important control channel (a super-frame

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allocation table), inter-system handover information, and basic properties of the random access channel (RACH). r Admission control: These functions admit new flow classes and renegotiate flow classes, accepting flow classes handed over from another BS. For more details of a possible implementation, see Chapter 10. r Establishment, maintenance and release of RRC connections: This includes the allocation of temporary identifiers between the UT and the WRAN and configuration of radio resources for RRC connection. r Security: This includes integrity protection and ciphering of RRC messages. r Establishment, maintenance, reconfiguration and release of flow classes for both unicast and multicast services. r Mobility: This includes paging, UT measurement reporting and control of the reporting for inter-cell and inter-RAT mobility, inter-cell handover, cell selection and re-selection, and UT context transfer between BSs. r Flow control between BS and RN. (Flow control to and from UTs is handled by the scheduler at the MAC layer.) r Cross-layer configuration of layer 2 protocol entities within the same node. In case of relaying, at least the following RRC functions are included in the RNLN :

r broadcast of system information; r QoS management functions; r paging. The RRC2 protocol includes the following control plane functions:

r establishment, maintenance and release of an RRC2 connection between the RN and WRAN; r conveying broadcast information to the RNs; r conveying information about establishment, maintenance, reconfiguration and release of flow classes for both unicast and multicast services for UTs served by the RN;

r conveying information related to resource partitioning; r conveying paging information; r cross-layer configuration of layer 2 protocol entities within the same node.

The RRC layer manages and controls the use of radio resources and therefore has links over control SAPs to all other layers, as shown in Figure 4.11. This interworking enables RRC to control and configure the other layers; it can receive measurement data from the lower layers as well as from the control functions in the other layers.

4.4.3 Layer 2 Layer 2 is split into three sublayers: the IP convergence layer (IPCL), the radio link control (RLC) layer, and the medium access control (MAC) layer. The main functions performed in these layers for the downlink and uplink are shown in Figure 4.13a and 4.13b, respectively. Both the IPCL and RLC layers are pure user plane protocols. The transmission may be to or from RNs or UTs.

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Figure 4.13 Layer 2 overview: (a) in the downlink and (b) in the uplink.

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4.4.3.1 IP Convergence Layer The IP Convergence Layer (IPCL) supports transfer of user data. IPCL receives an IPCL service data unit (SDU) and forwards it to the RLC layer and vice versa. The IP Convergence Layer is discussed in more detail in Chapter 10. The IPCL protocol supports header compression, decompression, ciphering, and insequence delivery of upper layer PDUs. Reordering is performed by the GWIPA in order to handle potential out-of-order packets that may arrive during a handover due to the different transmission delays of the packets from the involved BSs. Additionally, the IPCL performs duplicate detection of lower-layer SDUs. Duplication of packets may occur in either the uplink or the downlink direction when the acknowledgement of correctly received packets is not received by the transmitter, which then performs an unnecessary retransmission of the same packet. This layer also supports reordering of the downlink RLC SDUs at least during handover. Such reordering is performed by the UT in order to handle potential out-of-order packets that may happen during handover due to early forwarding of packets from the GWIPA . 4.4.3.2 Radio Link Control Layer The radio link control (RLC) layer protocol supports an unacknowledged mode (UM) and an acknowledged mode (AM). Whether UM or AM is used needs to be configured per flow class. Furthermore, the RLC layer supports segmentation and concatenation of RLC SDUs. Depending on the scheduler decision, a certain amount of data is selected from the RLC SDU buffer and segmented or concatenated, depending on the size of the SDUs. This selected data block becomes an RLC PDU to which a sequence number is assigned. This means that one transport block contains a single RLC PDU per flow class. However, there are two exception cases in which the RLC PDU is re-segmented:

r A retransmission PDU does not fit into the new transport block. r A received PDU at an RN does not fit entirely into the new transport block. In these two cases, a new sequence number is appended to the re-segmented packets by means of an extension header. The number of re-segmentations is not limited. It was shown that it is beneficial to keep the same RLC sequence number space on the entire path between the base station and the user terminal (see [WIN2D61314, Section 5.1.1] for further details) to enable fast retransmissions after a user terminal has moved from one node to another node within the relay-enhanced cell (REC). Hence, RLC SDUs are not re-assembled in the relay node and only re-segmentations are allowed, see Figure 4.14. In order to allow the RLC SDU to be reassembled at the receiver, the RLC header carries the required segmentation, re-segmentation and concatenation information. The RLC sequence number is also used at the receiver for in-sequence delivery to the RLC SDU reassembly entity. Details of the segmentation or reassembly process, its interaction with multiplexing at the MAC layer, and the associated signalling are described in [WIN2D61314, Section 5.1]. In AM, RLC is responsible for correcting residual H-ARQ errors by operating another ARQ protocol, the RLC-ARQ. The ARQ retransmission units are RLC PDUs or RLC PDU segments.


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Figure 4.14 RLC layer at the relay node (R-ARQ refers to relay ARQ, see Section 8.5.4).

If an RLC retransmission is required and the radio quality has changed significantly compared to the original RLC transmission, then the RLC protocol is able to perform a re-segmentation. In that case, RLC segments a PDU into smaller PDU segments. In case of relaying, the ARQ operation needs to be enhanced (e.g. with relay ARQ) in order to be able to determine which packets have been correctly received by the UT and RN (see Figure 4.14). Details of the ARQ operation, including its interactions with H-ARQ, are described in Section 8.5.4. Moreover, in case of relaying, the RLC also performs forwarding (i.e. mapping of an incoming SAP to an outgoing SAP). Finally RLC provides a means for protocol error detection and recovery (e.g. reset), duplicate detection, and SDU discard. 4.4.3.3 Medium Access Control Layer The WINNER medium access control (MAC) layer performs three main tasks:

r scheduling, which controls the transmission on the timescales of the frames; r multiplexing and demultiplexing; r H-ARQ, i.e. a retransmission procedure over one hop that uses incremental redundancy. The allocation of transmission resources is controlled by the network, not by the terminals, since we thereby attain the highest spectral efficiency. At the shortest timescales, this control is performed by schedulers at the MAC layer of the BSLN and RNLN . The schedulers receive inputs and constraints from the RRM functions, which perform resource allocation and flow control at slower timescales. As depicted in Figure 4.15, the scheduler controls the complete transmission chain on a packet-by-packet basis. It controls the segmentation at the RLC layer, the multiplexing at the MAC layer and the coding, modulation, multi-antenna processing and mapping onto transmission resources that are performed at the physical layer. The physical layer itself is

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Figure 4.15 Relationship of the MAC layer with other layers (from [WDK+09]). (Reproduced by Permission of IEEE © 2009).

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completely controlled by the scheduler in the MAC layer. This fast and tight interaction is possible since the RLC, MAC and PHY layers of a node are always assumed to be co-located and can therefore interact with negligible delays [WDK+08].1 If, in addition, several radios that use multiple bands are co-located, the scheduler can control the transmission resources within all the radio bands by controlling the multiple co-located physical layers.2 This particular functionality is called a multiband scheduler. A more detailed functional description of the scheduler is given in Section 4.4.3.3. Super-frames, Frames, Slots and Chunks The super-frame is a time–frequency–spatial resource allocation unit of the WINNER system concept. It fulfils two purposes:

r Each super-frame contains uplink and downlink synchronisation pilots that are used for self-organizing network synchronisation [WIN2D233]. The super-frame duration therefore scales the reaction time of this mechanism. r The resource partitioning (the division of transmission resources between cells and between RNs and BSs) is assumed to be specified one super-frame in advance. This provides a stable background for the resource allocation that is performed by the scheduler. The super-frame is assumed to have equal duration in the WINNER FDD and TDD modes. This facilitates inter-mode cooperation and multiband transmission. A super-frame consists of a short preamble with OFDM symbols used for pilots, followed by n frames. The n parameter is set to the sample value of eight in the sequel and for performance evaluations. In the frequency dimension, the super-frame comprises all frequencies (not necessarily adjacent) that are used within a cell. A frame is a temporal resource unit. The frame duration was set to the same value in the WINNER FDD and TDD modes. The frame duration contains two slots:

r In TDD mode, a frame consists of a downlink transmission slot followed by an uplink slot, separated by duplex guard-times.

r In FDD mode, one set of half-duplex terminals would receive downlinks in the first slot and transmit in the uplink in the second slot. A second set of terminals could do the opposite, since an FDD base station can be assumed to use full duplex. Full-duplex FDD terminals could transmit and receive in both slots, which doubles the maximal data rate. The super-frame structure (shown in Figure 4.16) has properties developed in WINNER Phase I [SSK06]. In the final WINNER II system concept, the super-frame was modified as follows:

r No contention-based uplink (DAC) channel is used, so separate transmission resources are not set aside for this purpose.

r The downlink physical broadcast channel (PBCH) and the uplink physical random access channel (PRACH) are no longer included in the preamble of each super-frame. This increases 1 The

scheduler of a BS or an RN will also control the resource allocation used for uplink transmission from UTs. be more precise, the MAC layer scheduler can control all PHY layer resources that can be controlled on a frame timescale without additional signalling delays. This could be used for co-located base stations that use different spectrum resources (FDD or TDD). 2 To

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Figure 4.16 Super-frame for the WINNER TDD mode, with uplink:downlink asymmetry ratio 1:1. Light slots are for downlinks; dark slots are uplinks; and white bands represent duplex guard times between downlink and uplink slots.

the flexibility, since the timescale for broadcast transmission and random access opportunities are decoupled from the super-frame timescale. The broadcast channel is transmitted at a prespecified position every mth super-frame. The random access channel is available in every super-frame, to reduce the delays for UT initial access to the network. r To eliminate unused frequency resources, the downlink and uplink network synchronisation pilots were moved into the first frame of the super-frame, in the beginning of the downlink and uplink slots. As in the case of the PBCH and PRACH, they utilise only spectral bands that are available over a wide geographical area, to facilitate multi-cell coordination. They each comprise three consecutive OFDM symbols. This corresponds to the minimum B-EFDMA block size of the reference design (see Chapter 9), so it fits well into the WINNER II frame structure. r Both the FDD and the TDD super-frame preambles include one OFDM symbol that contains uplink pilots in order to provide the BS or RN with short-term channel state information (see Chapter 6). The super-frames thus consist of an uplink pilot preamble of one OFDM symbol followed by n frames. Figure 4.16 illustrates the case of TDD transmission with uplink:downlink asymmetry in a ratio of 1:1. With cyclic-prefix OFDM, the smallest time–frequency unit consists of one subcarrier by one OFDM symbol duration, here denoted as a channel symbol. Rectangular sets of ns subcarriers by nt OFDM symbols are assumed to be grouped into time–frequency units denoted chunks.


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Time

Figure 4.17 Chunk sizes used for evaluation in WINNER II [WIN2D6137]. The figures show a slot (half a frame) in each case, assuming 1:1 TDD asymmetry. (Reproduced by Permission of IEEE © 2009).

A chunk within one spatial layer is denoted a chunk layer. The dimensions of the chunks used for evaluations in WINNER II are illustrated in Figure 4.17. The frame duration is the same in the FDD and TDD modes and has been set to 0.6912 ms throughout the WINNER projects. The slot duration is half a frame in FDD and in TDD with asymmetry ratio 1:1 and is thus 0.3456 ms. The slot and frame durations are the basic scales of delays and reaction speeds of the WINNER MAC layer. Functionalities The MAC layer may multiplex RLC PDUs that belong to different flow classes. The resulting packets form retransmission units, handled by the H-ARQ function that works over each hop in a multi-hop transmission. The WINNER system supports small retransmission delays allowing H-ARQ to be invoked for most flow classes, including delay-sensitive flows. The whole procedure is controlled by the multi-layer scheduler. The MAC layer can multiplex segments that belong to different flow classes. The aim is to reduce the overhead, in particular for smaller packets and in transmissions over relay links. Multiplexing is allowed for segments (RLC PDUs) that are transmitted to the same logical node, for example a relay node. Segments to be transmitted to different logical nodes are not multiplexed. A MAC MUX header (see [WIN2D61314, Section 5.1.2]) is appended to an RLC PDU (which may be multiplexed), resulting in a MAC PDU. If link retransmission is to be used, a cyclic redundancy check (CRC) code sequence is added to the MAC PDU. This represents the MAC retransmission unit (RTU), or transport block. An overview of the segmentation, concatenation, and multiplexing process is given in Figure 4.18.

120

MAC (multiplexing)

IPCL SDU

MAC SDU

Multiplexing

RLC SDU

Transport Block

MAC PDU

RLC PDU

H

IPCL header

Concatenation

IPCL SDU

RLC SDU

H

RLC header RLC PDU

header Higher Layer Payload

Higher Layer PDU Flow Class 1

IPCL SDU

CR C

MAC header

Transport Block

MAC PDU

MAC SDU

RLC header RLC PDU

Segmentation

RLC SDU

IPCL header

H



Figure 4.18 Segmentation, concatenation, and multiplexing process from higher layer PDUs to transport blocks.

MAC header

RLC header

RLC (segmentation & concatenation)

IPCL header

IPCL (Header Compression & Ciphering)



C RC


121

The retransmission scheme used over one link is described in more detail in [WIN2D223, Section 4.2]. It combines forward error correction and automatic repeat requests (ARQ) in a scheme that uses incremental redundancy: 1. The RTU is encoded as one or several forward error correction (FEC) code blocks. The preferred WINNER coding schemes are used: r rate 1/4 convolutional coding is used for blocks with less than 200 payload bits; r quasi-cyclic block LDPC with mother code rate 1/3 is used for larger block sizes. Both are assumed to be systematic codes, so the FEC blocks consist of uncoded bits followed by redundancy bits. Assume that the first N3 bits of the FEC block are systematic bits (the uncoded segment) and the remaining bits are redundancy bits. 2. A H-ARQ segment is transmitted for all FEC blocks that comprise one RTU. The initial H-ARQ segment uses the first N2 ≥ N3 bits of the FEC block, where N3 /N2 is the code rate that is specified by the link adaptation. 3. The CRC code of the RTU is used to detect transmission errors. 4. If retransmission is required, additional parity bits, known as incremental redundancy (IR) blocks, are transmitted by additional H-ARQ segments. Each IR block uses a link adaptation that is appropriate for that transmission. Soft-bit combining is used at the receiver. Since the CRC code spans the whole RTU, incremental redundancy transmissions have to be performed for all FEC blocks that belong to the RTU. 5. If the end of the FEC block is reached without a correct reception, the H-ARQ process uses the FEC block cyclically from the beginning, producing additional IR blocks until a maximum allowed number of retransmissions is reached. This scheme provides a seamless transition from the use of incremental redundancy for a few transmissions to chase combining for many retransmissions. The size of the IR blocks can be adjusted to a fraction of the initial H-ARQ segment size, see [WIN2D223, Section 4.2]. This choice is signalled as outband information at the initial transmission. An N-channel, stop-and-wait protocol with one-bit feedback is used as the retransmission protocol for a flow class. This means that we allow each flow class to use up to N parallel retransmission channels. A new RTU can be transmitted if there are fewer than N outstanding unacknowledged RTUs, i.e. if at least one channel is not in use. A scheduler coordinating the RLC, MAC and PHY layers is located in each BS and RN, see Figure 4.15, to provide efficient resource allocation. The resource allocator allocates all available transmission resources within the slot (i.e. it distributes time–frequency–spatial resources to different UTs). Some resources may be pre-allocated over multiple slots for transmission of flows whose packets have regular properties. The BS always performs the channel-aware resource allocation, whereas flow-class prioritisation is always performed by the transmitting part. This means that:

r For the downlink, the scheduler performs resource allocation and prioritises between different UT flow classes within those resources.

r For the uplink, the scheduler in the BS (or RN) performs resource allocation, however, a simpler scheduler in the UTs prioritise between the different uplink flow classes for that UT.

r In case of cooperative relaying (downlink transmission from multiple RAPs), the scheduler

at one coordinating RAP performs the scheduling (payload-selection-resource allocation and

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mapping, and link adaptation). It signals these decisions to the schedulers in all participating RAPs (see Chapter 8 for more details on cooperative relaying). The scheduler also selects the transport format and the spatial transmission scheme individually for each H-ARQ segment or code block. The WINNER concept supports individual link adaptation within the chunk layers that are used to transmit one H-ARQ segment. The following constraints and inputs on the scheduler are static or vary only slowly over time:

r antenna resources at the network side, multi-antenna and other capabilities of the BS, RNs and UTs involved;

r restrictions on the resource allocation that originate from the RRM functions (spectrum functions, resource partitioning between RN/BS, interference avoidance between cells, use of cooperative transmission) and are consolidated by the constraint processor; r properties and QoS constraints defined by the flow class of the packets; r the total load situation and distribution of users in the cell and the resulting possibilities and restrictions on using spatial division multiple access (SDMA). The following constraints and inputs on the scheduler vary on a frame timescale:

r transmission requests for uplink transmissions; r queue lengths and transmission demand for each flow class; r CQI and CSI information for time–frequency–spatial transmission resources within the frame to be scheduled;

r the need to transmit packets with high priorities, such as IR blocks (retransmissions); r scheduling constraints from cooperative relaying: one node determines the transmission parameters; the schedulers at the involved RAPs receive these decisions as constraints on the scheduling allocation; r time-to-live information of individual packets in delay-critical transmissions (depending on the number of hops, the delay budget, queue occupancy and relay-link, flow-control input). The scheduling has the overall aim of satisfying the QoS constraints of all flow classes. Channel-aware scheduling can also allocate transmission resources that are advantageous for each transmission, to optimise the network capacity or the terminal power consumption. The scheduling structure supports joint decisions that solve the following tasks (the exact implementation is manufacturer dependent):

r payload selection: a decision on the flow classes to transmit in the scheduled slot; r resource allocation: allocation of time–frequency–spatial transmission resources within a slot to specific MAC PDUs;

r resource mapping: a decision that determines the mapping of H-ARQ segments onto a set of transmission resources, using a set of link adaptation parameters (this decision is optimised jointly with a decision on segmentation and concatenation and the choice of link adaptation parameters); r link adaptation: a decision on the modulation parameters, the spatial layer or beam and the code rate used for transmitting a code block.


Multicast Logical Channel

123

Delay

Resources for each scheduled Logical & Transport Channel

Buffer size

UT Logical Channels

Delay

Scheduler

Flow class attributes

Restrictions (RRM, UT cap.,...)

Constraint Processor

Buffer size Flow class attributes Number of bits CQI report

UT

Resource

Link Adapter & Spatial Scheme Controller

HARQ feedback

Figure 4.19 Scheduler: main inputs and outputs (from [DWK07]). (Reproduced by Permission of IEEE © 2009).

The scheduler provides detailed allocation of packet segments to physical time– frequency–spatial transmission resources, including spatial link adaptation. This includes segmentation and concatenation commands to the RLC layer, multiplexing commands to the MAC layer, and instructions on the modulation, encoding and puncturing, spatial scheme, and time–frequency resource mapping performed at the physical layer. The scheduler also provides outband frame control information (i.e. control information not carried within any protocol header) that enables the receiver to decode the relevant part of a frame. This information is transmitted on the physical channels PDFCC and PUCH for the downlink and uplink directions, respectively. The outband frame control information and its corresponding overhead is discussed in more detail in [SSD08] and in [WIN2D61314, Annex]. Figure 4.19 illustrates the main input and output parameters of the scheduler in downlinks. It shows how the scheduler can be partitioned into a constraint pre-processor that handles various constraints on the transmission resources, a main scheduler and a sub-function that focuses on optimizing the link adaptation. Transmission sequences and transmission timing are discussed further in [WIN2D61314, Section 5.2]. A key functionality of the WINNER requirement to be able to handle fragmented spectrum efficiently (see Section 2.7) is the multiband scheduling concept. When the MAC layer controls several physical layers that are co-located so that control transmission delays are negligible, these multiple bands represent a widened resource pool as seen from the scheduler. One constraint imposed by the WINNER multiband scheduling concept is that the UT should never have to transmit and receive simultaneously in several bands that are not covered by the same radio (the same FFT), to simplify the terminal design. This means that MAC PDUs and their retransmissions should be transmitted in either band, not in all bands simultaneously.

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Downlink only Downlink only

Uplink only

Downlink or Uplink

(Multicast)

Logical Channel Types – characterised by the type of the information transmitted LPCCH

LBCCH

LCCC

LDTCH

LDCCH

LMTCH

LMCCH

Transport Channel Types – characterised by how and with what characteristics the information is transmitted TPCH

TBCH

TRAC

TSCH

TMCH

Figure 4.20 Mapping between logical channels and transport channels.

The scheduler may however decide to switch the transmission to or from one UT to another band, to obtain better load balancing. A H-ARQ channel is regarded as being associated with one particular radio band. When the transmission is switched to another band, the H-ARQ channel and state are associated with this new band and H-ARQ transmissions for previously transmitted packets proceed over the new band.3 For a detailed discussion of the WINNER multiband scheduling concept and its relation to flexible spectrum use, see Section 11.4.1. Logical Channels and Transport Channels Logical channels define types of communication between RLC peers. They are characterised by the type of information transmitted. Some are for downlink information, some for uplinks, and some are bi-directional. Transport channels define types of communication between MAC peers. They are characterised by how and with what characteristics the information is transmitted. Physical channels describe different distinct sets of physical resources that are required by various transmissions. They are described in Section 4.4.4.2. Figure 4.20 shows how the logical and transport channels are used in the WINNER concept. The logical channels are:

r Broadcast control channel (LBCCH): A downlink channel for broadcasting system control information. The LBCCH information is split into two: a static part and a dynamic part. The static part is transmitted every mth super-frame using the same pre-allocated physical resources. The location of the dynamic part is signalled in the static part. The dynamic part is transmitted every yth super-frame (where y is a multiple of m), using the shared transport channel TSCH. The static part contains information needed for an unknown UT to identify basic cell properties and to be able to initiate communication, e.g cell ID, operator ID, and FDD/TDD duplex mode information. Other candidates for inclusion in the static

3 This is possible with insignificant delays due to the assumed physical co-location of the radios (the physical layers). Multiband transmission that uses radios that are not co-located has to use the RRM handover functionality.


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part are spectrum-sharing restriction parameters and RACH properties. In the dynamic part, parameters such as transmission power mask are signalled. r Paging control channel (LPCCH): A downlink channel that transfers paging information (Chapter 10). This channel is used when the network does not have information about the location cell of the UT. r Common control channel (LCCCH): This channel is used by UTs that have no RRC connection with the network. It is used on initial access to the network. r Dedicated control channel (LDCCH): A point-to-point bi-directional channel that transmits dedicated control information between a UT and the network. It is used by UTs with an RRC connection (e.g. for messages setting up links between a RAP and a UT). Various RRM mechanisms (see Chapter 10) also use the LDCCH. r Multicast control channel (LMCCH): A point-to-multipoint downlink channel used for transmitting multicast and broadcast message service (MBMS) control information from the network to the UT, for one or several transport multicast channels (TMCHs). This channel is only used by UTs that receive MBMS. Messages for the setup and modification of multicast groups use the LMCCH. r Dedicated traffic channel (LDTCH): A point-to-point channel, dedicated to one UT, for the transfer of user information. An LDTCH can exist in both uplink and downlink. r Multicast traffic channel (LMTCH): A point-to-multipoint downlink channel for transmitting traffic data from the network to the UT. This channel is used only by UTs that receive MBMS services. The transport channels are:

r Broadcast channel (TBCH): A downlink channel for broadcasting system information, to all terminals inside the cell’s coverage area.

r Paging channel (TPCH): A downlink channel used for broadcast of paging information into an entire cell. It contains a paging indicator (PI) and a paging message (PM). The PI is a short message that indicates the range of UT addresses being paged within this cell. The PM contains the paging reason (>3 bits), the paging domain (one bit) and the paging identity (32 bits). r Random access channel (TRAC): A contention-based uplink channel for initial access to the network. This channel is used to obtain timing synchronisation (asynchronous random access). r Shared channel (TSCH): A point-to-point data channel for both the uplink and the downlink for both user and control data. It is possible to broadcast this channel in the downlink over the entire cell. r Multicast channel (TMCH): A separate transport channel for multicast transmission. It is to be utilised by MBMS. This channel is broadcast in the entire coverage area of the cell. Combining of multicast transmissions from multiple cells is supported.

4.4.4 Layer 1: Physical Layer The physical (PHY) layer handles the physical transmission of flows, measurements and control signalling that is directly related to the radio interface. The PHY layer is not separated

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into user plane and control plane, since it is assumed that all control functionality of the PHY layer resides within the MAC layer. The main characteristics of the PHY layer are discussed in detail in Chapters 5, 6, and 7. Some general aspects are discussed in the following sections. 4.4.4.1 Control Signalling Two main principles have been used in the WINNER concept for selecting the physical format for transmitting control signalling and measurement reports. Is the control signalling urgent or non-urgent, in the sense that timing allows it to use existing H-ARQ schemes?

r Any non-urgent control messages and measurement reports are treated in the MAC layer as packets to be transmitted using the TSCH transport channel. H-ARQ is used to reduce and control the error probability. r Urgent control messages need special FEC protection and may also have to be placed in special positions within the frame for feedback-loop timing reasons. Special control channels (PDCFC, PDFCC and PUCH) have been defined for these urgent control messages (see Section 4.4.4.2). Here, we use non-frequency-adaptive transmission with small block sizes to maximise frequency diversity and to obtain precise message timing within the frames, see Section 9.2.3. Whenever possible, frequency diversity is to be combined with spatial- or polarisation-diversity schemes to reduce the error probability.

Does the control signalling need to be broadcast or can it be multicast or unicast to UTs or groups of UTs?

r An optimisation is assumed to select the best type of transmission for each control message. Broadcasting results in transmission with the lowest spectral efficiency, since it has to be adjusted to the worst user, with possible not well-known SINR. Two channels that need to be broadcast have been defined: the physical broadcast channel (PBCH) and PDFCC for frame control messages. To keep the control overhead acceptable, it is important to minimise the payload that needs to be transmitted over these two channels. Downlink control messages to groups of users (multicast groups) may be transmitted individually to each user (unicast) or multicast. Multicasting is the most efficient scheme for downlink control information if the multicast groups contain sufficiently many members. Otherwise, control messages are preferably unicast. The limit between urgent and non-urgent messages is determined by the possibility of performing at least one H-ARQ retransmission. With the delays indicated in [WIN2D61314, Section 5.2], this limit is around 4 ms. The various measurements required for RRM and for MAC/PHY control, and their delay requirements, are summarised in [WIN2D233, Section 5]. WINNER supports two major transmission schemes that use frequency-adaptive or nonfrequency-adaptive transmission [SSD08] (see Chapter 9). Although they have differing


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Table 4.1 Control information and assumptions. Information bits per message

Information

Link direction

H-ARQ information

DL, UL

3+3

Per H-ARQ channel use

Transport block size, code rate Modulation information

DL, UL

5

DL, UL

2

Spatial processing DL, UL scheme Cell-specific user DL, UL address H-ARQ feedback DL, UL

5

Every transport block For every chunk layer in case of frequencyadaptive transmission. For every transport block in case of non-frequency adaptive transmission. Per MAC PDU

12

Per user or per flow Implicitly signalled

1

Per H-ARQ channel use Per user b Per user Per frame

CQI feedback CSI feedback Chunk allocation table

UL UL DL

flexible length

Message frequency

Comment 3-bit H-ARQ-ID: 2 bits for redundancy version, 1 bit new data indicator a

contains DL allocation and UL allocation of chunk layers to users

a

Assuming an asynchronous N-Channel stop-and-wait protocol supporting incremental redundancy. Update rates in frequency and time are not considered in this table, as they can be adjusted according to coherence properties of the channel as explained in [WIN1D24]. b

control signalling requirements, it has proved possible to design one control channel with different parameterisations that controls both schemes with acceptable downlink overhead. Table 4.1 gives a summary of the most important urgent control messages that need to be transmitted, their message sizes and their message frequency or urgency. Two physical control channels, PDCFC and PDFCC have been defined to support this frame control signalling. The control signalling scheme of WINNER allows flexible configuration of the control information on the PDCFC and PDFCC to a large range of operating conditions (e.g. ranging from a few high-rate users to many low-rate users and from a full load to a low load). In each frame, a minimal amount of information is broadcast to all users using a safe (but

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resource-demanding) coding and modulation scheme. More detailed information is transmitted to groups of users with differing SINRs, using tailored link adaptation. The total overhead is further minimised by using implicit information, as well as the adaptation of the control message format between a combinatorial and table-based approach to signal the resource allocation. Using such a flexible-length, multi-part, control message, the overhead can be kept below 24 % while having full scheduling flexibility for up to 1280 users in a 100 MHz [SSD08]. Further information on the transmission control sequences and their timing for downlink and uplink can be found in Section 9.2.4. It should be noted that a semi-static allocation of transmission resources can be used for some flow classes and users. This means that a fixed set of transmission resources (chunks or blocks and link adaptation parameters) is pre-allocated in multiple frames, which reduces the control overhead significantly as illustrated in [WIN2D61311, Section 4.7]. This is particularly possible for voice-over-IP flows, which are destined for slow-moving users. 4.4.4.2 Physical Channels and Mappings to Transport Channels This section gives a brief overview of the transmission technology and location in the superframe for the different physical channels. Detailed explanation of the chosen transmission technology can be found in Chapters 5, 7, and 9. Physical Broadcast Channel The physical broadcast channel (PBCH) is a downlink physical channel for broadcasting system information, etc., to all terminals inside the cell coverage area.

r Transmission technology: B-EFDMA allocation with smallest block size (four subcarriers by three OFDM symbols), to maximise frequency diversity, as described in Section 9.2.3. PBCH is sent by cell-wide spatial transmission that uses (Alamouti) space–frequency coding to achieve spatial diversity. 4-QAM with rate 1/4 convolutional coding and 6 x repetition coding is used, with a target minimum downlink SINR of −8 dB. r Position in super-frame (Figure 4.16): 3-OFDM symbol part of a downlink slot in frame j of every mth super-frame. It uses only frequency resources that are available within a wide area (multiple cells). The PBCH transmissions from different RAPs can potentially use a frequency re-use pattern to minimise interference between PBCHs of neighbouring BSs and RNs. Physical Downlink Control Format Indicator Channel The physical downlink control format indicator channel (PDCFC) is used for transmitting parameters indicating the location and layout of the PDFCC channel. As outlined in [SSD08; WIN2D61314], the downlink control information that signals the frame layout for downlinks and uplinks and for frequency-adaptive and non-frequencyadaptive transmission, is composed of several parts to minimise the control overhead. The PDCFC message contains a broadcast configuration table, CT, and an optional broadcast control message length indicator, LI. The PDCFC message is broadcast to all involved terminals.


129

r Transmission technology: The transmissions use B-EFDMA non-frequency-adaptive transmission with small (4 × 3) block sizes and spatial diversity where possible. 4-QAM with rate 1 /4 convolutional coding is combined with 6 x repetition coding, to reach a target minimum downlink SINR of −8 dB. r Position in super-frame: Uses the set of downlink resources within the frame that are allocated to non-frequency-adaptive transmission. It is transmitted in the earliest three OFDM symbols of each downlink slot. Physical Downlink Frame Control Channel The physical downlink frame control channel (PDFCC) is used to signal information in a slot of a frame related to downlink data transmission within that slot and uplink data transmission in the subsequent slot. The PDFCC is used to define the resource allocation both for frequencyadaptive and non-frequency-adaptive transmission. The allocation tables (AT) and transport format tables (TFT) are transmitted on the PDFCC. The following information is conveyed on this channel:

r DL scheduling control: User terminal or relay node ID, resource allocation for PNDC that identifies the chunks or blocks have been allocated, the modulation and coding scheme, H-ARQ-related information (e.g. H-ARQ process ID and redundancy version), and ACK or NACK related to UL transmission. r UL scheduling grant: User terminal or relay node ID, resource allocation for PNDC that identifies the chunks that have been allocated, the modulation and coding scheme, and the (slow) uplink power control command. Not all types of table need be used in a particular parameterisation. Different table layouts are used for specifying frequency-adaptive and non-frequency-adaptive transmission. In the case of frequency-adaptive transmission, different table layouts are used when there are a few participating users or many users (for details, see [SDD08; WIN2D61314, Annex A.1]).

r Transmission technology: Uses B-EFDMA non-frequency-adaptive transmission with small (4 × 3) block sizes and spatial diversity where possible. Convolutional coding is used combined with repetition coding. The allocation and transport format tables are partitioned into sub-tables destined for different groups of users with different SINRs. Each such subtable is encoded with an appropriate code rate to limit the downlink control overhead. r Position in super-frame: Uses the set of downlink resources within the frame that are allocated to non-frequency-adaptive transmission. Tables for control of the downlink are transmitted in the earliest three OFDM symbols of each downlink slot. Tables for control of the subsequent uplink are transmitted in the following three OFDM symbols (numbers 4 to 6) of the slot. Physical Frequency-adaptive Data Channel The physical frequency-adaptive data channel (PADC) is used to transmit point-to-point, user data in downlinks and uplinks. A PDFCC is associated with each PADC to control the transmission.

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r Transmission technology: Modulation (BPSK, 4-QAM, 16-QAM, 64-QAM, 256-QAM) adjusted individually per chunk layer. An average code rate is computed for all chunk layers and is then used for the whole H-ARQ block. Convolutional codes are used for FEC block sizes of less than 200 payload bits. Quasi-cyclic block LDPC codes are used for larger FEC block sizes. r Position within super-frame: PADC uses a set of chunks within the slot that is pre-allocated for frequency-adaptive transmission by the resource division function. Physical Non-frequency-adaptive Data Channel The physical non-frequency-adaptive data channel (PNDC) is used for point-to-point, user-data transfer in downlinks and uplinks. A PDFCC is associated with each PNDC.

r Transmission technology: One modulation (BPSK, 4-QAM, 16-QAM, 46-QAM, or 256QAM) and code rate is chosen for the whole H-ARQ block. Convolutional codes used for FEC block sizes less than 200 payload bits. Quasi-cyclic block LDPC codes are used for larger FEC block sizes. r Position within super-frame: PNDC uses a set of chunks within the slot that is pre-allocated for non-frequency-adaptive transmission by the resource division function. The chunks are regularly spaced in frequency. Within that set, time–frequency blocks of size less than or equal to one chunk are allocated to transmissions. One code block uses a set of blocks that are all of the same size and are regularly spaced in frequency within one single slot. In downlinks, the scheme is denoted B-EFDMA. In uplinks, DFT-precoding is used (B-IFDMA) [SFF+07]. Please see Section 9.2.3 for a detailed discussion of these multiple access schemes. Physical Multicast Broadcast Channel The physical multicast broadcast channel (PMBC) is used to carry MBMS services. It uses B-EFDMA non-frequency-adaptive transmission with a modulation and code rate adjusted to the user with worst SINR in the multicast group. The PMBC transmission can support cooperative relaying.

r Transmission technology: As for PNDC downlinks, but the modulation and code rate is adjusted to the user with worst SINR within the multicast group.

r Position within super-frame: As for PNDC downlinks. Uses the set of downlink resources within frame that are allocated to non-frequency-adaptive transmission. Physical Uplink Control Channel The physical uplink control channel (PUCH) is used for urgent uplink control messages. The PUCH resources are pre-allocated to the UT and hence no UT ID needs to be conveyed in this message. The PUCH channel contains the H-ARQ ACK/NACK, triggered by DL data transmission and CQI messages. If no PADC or PDNC resources are assigned, it also contains scheduling requests. If PADC or PDNC uplink resources are assigned, then scheduling requests may be multiplexed into the PADC or the PDNC.


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r Transmission technology: The transmission is protected by convolutional coding in combination with repetition coding, adjusted to the average uplink SINR of the UT. It does not use a H-ARQ process. r Position within super-frame: Each active UT is given a small allocation in the uplink slot of each frame for the PUCH. The allocation is slowly time-varying with the SINR and the traffic load. B-IFDMA allocations are used. The allocation of PUCH resources is performed on initial access and is modified on a slow timescale when the traffic load of the UT varies significantly. Since the PUCH is limited to only few bits, detailed information needs to be carried on other physical channels, e.g. the PNDC. Physical Random Access Channel The physical random access channel (PRACH) is a contention-based uplink physical channel needed to acquire time alignment. The amount of resources used for PRACH is for further study, but it is important to schedule the PRACH resources in neighbouring cells such that the PRACH channel does not cause severe interference, especially to the PNDC and PADC channels.

r Transmission technology: An uplink PRACH transmission uses 4-QAM in one of the two OFDM symbols within the allocated uplink time slot.

r Position within super-frame: Uses a time slot with a duration of three OFDM symbols, that comprises two transmission OFDM symbol durations and synchronisation error guard times in both directions. This slot is positioned in the uplink slot of the jth frame of the super-frame. PRACH uses only frequency resources that are available within a wide area (just as with PBCH). 4.4.4.3 Synchronisation Pilots The downlink slot of the first frame in each super-frame contains downlink network synchronisation pilots that use the three OFDM symbols in the downlink slot. The uplink slot of the first frame of each super-frame contains uplink network synchronisation pilots that utilise the three OFDM symbols of the uplink slot. These pilots utilise only spectral bands that are available over a wide geographical area, to facilitate multi-cell coordination. In the allocated OFDM symbols, they utilise the whole of these bands. Payload transmission may proceed simultaneously in other bands.4 Figure 4.21 shows the mapping between transport channels and some of the physical channels. The remaining physical channels described above are used for control signalling between Layer 1 and Layer 2 and are not shown in Figure 4.21. The TBCH, TMCH and TRAC transport channels are directly mapped onto the corresponding physical channels. The transport shared channel (TSCH) may, for its physical transmission, use frequencyadaptive transmission (PADC), non-frequency-adaptive transmission (PNDC) or, for urgent uplink control and CQI measurements, the physical uplink control channel (PUCH).

4 To reduce interference to such transmissions, the network synchronisation pilots should use a transmit filter to suppress their interference within other sub-bands.

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Downlink only

TBCH

PBCH

TMCH

PMBC

Uplink only

Downlink or Uplink

TPCH

TSCH

PNDC

PADC

TRAC

PUCH

PRAC

Figure 4.21 Mapping of transport channels to physical channels (physical control channels are not shown). (Reproduced by Permission of IEEE © 2009).

The paging transport channel (TPCH) uses non-frequency-adaptive downlink transmission at physical locations that are known to all UTs. It uses low rate coding and modulation that is appropriate for broadcast control messages. No H-ARQ process is involved.

4.5 Conclusion This chapter has provided an overview of the WINNER system concept and architecture, which has been designed according to the principles of flexibility and cross-layer optimisation. Operation in a shared spectrum, relaying, low latency, optimised physical layer design, as well as advanced multi-antenna and interference control techniques are an integral part of the concept. A flat logical node architecture based on the pool concept enables scalable and efficient deployments, as well as equipment sharing, MBMS based on SFN, and multiband transmissions. A description of the logical nodes, protocols and channels provides a first overview and the necessary framework and terminology for the remaining chapters.

Acknowledgements The authors would like to thank all their colleagues from the WINNER I and II projects who contributed to the WINNER system concept. In particular, we acknowledge the contributions of Gunther Auer, Kari Kalliojärvi, Jijun Luo, Tommy Svensson, and Carl Wijting.

References [3GPP08]

[DWK07]

3GPP (2008) Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Stage 2, TR 36.300, V8.5.0, Technical Specification Group Radio Access Network, 3GPP. Doppler, K., Wijting, C.S., and Kermoal, J.-P. (2007) ‘Multi-band Scheduler for Future Communication Systems’, Proc. International Conference on Wireless Communications Networking and Mobile Computing (WICOM), pp. 6744–8.


[PDH+06]

133

Pollard, A., Döttling, M., von Häfen, J., Schulz, D. and Zimmermann, E. (2006) ‘WINNER: Towards Ubiquitous Wireless Access’, Proc. VTC 2006, pp. 42–6, IEEE, Melbourne, Australia. [SFF+07] Svensson, T., Frank, T., Falconer, D., Sternad, M., Costa, E. and Klein, A. (2007) ‘B-IFDMA: A Power Efficient Multiple Access Scheme for Non-frequency-adaptive Transmission’, Proc. 16th IST Mobile & Wireless Communications Summit, Budapest, Hungary. [SSD08] Sternad, M., Svensson, T. and Döttling, M. (2008), ‘Resource Allocation and Control Signalling in the WINNER Flexible MAC Concept’, Proc. IEEE VTC 2008 Fall, Calgary, Canada. [SSK06] Sternad, M., Svensson, T. and Klang, G. (2006) ‘The WINNER B3G System MAC Concept’, Proc. IEEE VTC 2006 Fall, pp. 1–5, Montreal, Canada. [WDK+08] Wijting, C., Doppler, K., Kalliojärvi, K., Johansson, N., Nyström, J., Olsson, M., Osseiran, A., Döttling, M., Luo, J., Svensson, T., Sternad, M., Auer, G., Lestable, T. and Pfletschinger, S. (2008), ‘WINNER II System Concept: Advanced Radio Technologies for Future Wireless Systems’, Proc. ICT-Mobile Summit 2008 Conference, Paul Cunningham and Miriam Cunningham (eds), Sweden. [WDK+09] Wijting, C., Doppler, K., Kalliojärvi, K., Johansson, N., Nyström, J., Olsson, M., Osseiran, A., Döttling, M., Luo, J., Svensson, T., Sternad, M., Auer, G., Lestable, T. and Pfletschinger, S. (2009), ‘Key Technologies for IMT-Advanced Mobile Communication Systems’, submitted to IEEE Wireless Communication Magazine. [WIN1D210] WINNER I (2005) IST-2003-507581 Final Report on identified RI key technologies, system concept, and their assessment, Deliverable D2.10, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D223] WINNER II (2007) IST-4-027756 Modulation and Coding Schemes for WINNER-II System, Deliverable D2.2.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN1D24] WINNER I (2004) IST-2003-507581 Assessment of adaptive transmission technologies, Deliverable D2.4, February 2005, viewed 20 June 2009, http://projects. celtic-initiative.org/winner+/deliverables older.html. [WIN2D233] WINNER II (2007) IST-4-027756 Link Level Procedures for the WINNER System, Deliverable D2.3.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D341] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined SpatialTemporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D351] WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D352] WINNER II (2007) IST-4-027756 Assessment of relay based deployment concepts and detailed description of multi-hop capable RAN protocols as input for the concept group work, Deliverable D3.5.2, June 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D353] WINNER II (2007) IST-4-027756 Final assessment of relaying concepts for all CGs scenarios under consideration of related WINNER L1 and L2 protocol functions, Deliverable D3.5.3, September 2007, viewed 20 June 2009, http://projects.celtic-initiative .org/winner+. [WIN2D472] WINNER II (2007) IST-4-027756 Interference avoidance concepts, Deliverable D4.7.2, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+. [WIN2D473] WINNER II (2007) IST-4-027756 Smart antenna based interference mitigation, Deliverable D4.7.3, June 2007, viewed 20 June 2009, http://projects.celtic-initiative .org/winner+. [WIN2D481] WINNER II (2006) IST-4-027756 WINNER II Intramode and Intermode Cooperation Schemes Definition, Deliverable D4.8.1, June 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6114] WINNER II (2007) IST-4-027756 Final WINNER II System Requirements, Deliverable D6.11.4, July 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+.

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[WIN2D6137] WINNER II (2006) IST-4-027756 Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, December 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D61311] WINNER II (2007) IST-4-027756 Final CG ‘metropolitan area’ description for integration into overall System Concept and assessment of key technologies, Deliverable D6.13.11, November 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D61314] WINNER II (2008) IST-4-027756 WINNER II System Concept Description, Deliverable D6.13.14, January 2008, viewed 20 June 2009, http://projects.celticinitiative.org/winner+.

5 Modulation and Coding Techniques Thierry Lestable1 and Stephan Pfletschinger2 1 2

Samsung CTTC

5.1 Introduction This chapter captures leading-edge technologies related to channel coding, link adaptation and hybrid automatic repeat request (H-ARQ) algorithms that are essential to the WINNER System. Refinements and optimizations of advanced channel coding candidates (Section 5.3), specifically duo-binary turbo codes (DBTC) and quasi-cyclic block low-density parity-check (QCBLDPC) codes, have been focusing on enabling key features such as rate compatibility through puncturing (RCP) to make full use of the advantages of an incremental redundancy (IR) hybrid-ARQ scheme (Type-II), together with targeting higher codeword lengths (lifting of LDPC codes). Particular attention is paid to coding of control signalling information (short packets), especially focusing on broadcast channel (BCH) robustness, which directly impacts the coverage capabilities of the system. For this purpose, an existing solution based on optimum distance spectrum (ODS) convolutional codes is highlighted and promoted as a suitable and promising candidate. A brand new link adaptation algorithm (Section 5.4), based on a mutual-information approach, has been proposed, designed, and tuned with respect to the advanced channel coding candidates. In-depth evaluations and comparisons, taking into account multiple impairments (e.g. prediction errors) outline the outstanding performance enhancement brought by such new and innovative approach. Finally, in Section 5.5, an innovative framework of H-ARQ is introduced for the first time, leading to flexible and efficient handling of joint link adaptation, incremental redundancy and repetition coding. This enables the thorough evaluation of achievable throughput and delay whilst combining link adaptation with H-ARQ. Radio Technologies and Concepts for IMT-Advanced Martin Döttling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

136


Figure 5.1 System model of BICM.

5.2 Basic Modulation and Coding Scheme The WINNER air interface has to adapt to a wide range of radio environments and thus to a wide range of SNR values. For high spectral efficiency, in general, coding and modulation have to be optimized jointly. This paradigm led to the development of trellis coded modulation (TCM) and was successfully applied in wireline channels. The drawback of these coded modulation schemes is the associated high system complexity and therefore it is highly desirable to treat coding and modulation separately. Fortunately, this separation is possible with hardly any compromise in performance and it consists in decoupling the coding from the modulation by a pseudo-random interleaver. This scheme is known as bit-interleaved coded modulation (BICM) and is depicted in Figure 5.1: the coded bits cn are permuted by the interleaver and then mapped to QAM symbols. A very detailed analysis of BICM in terms of capacity, cut-off rate and error probabilities was carried out by Caire et al. [CTB98]. This seminal work showed that BICM incurs hardly any loss in performance with respect to the more general coded modulation. This holds especially for fading channels and also applies to the outage capacity for quasi-static channels, as has been shown by Ekbal et al. [ESC03]. Figure 5.2 shows the capacity of BICM on an AWGN channel in comparison to the inputconstraint capacity of QAM, denoted as CM capacity. These capacities are given by: 2 ⎤ √ ˆ exp − w + γ (x − x) R1 1 ⎢ x∈χ ⎥ ˆ ⎥ ld IBICM,R1 (γ ) = R1 − 2−R1 E⎢ ⎣ √ 2 ⎦ q=1 b=0 x∈χbq w ˆ exp − w + γ (x − x) ⎡

q

ICM,R1 (γ ) = R1 − 2−R1

E ld

x∈χ w

ˆ b x∈χ

(5.1)

2 √ ˆ + |w|2 exp − w + γ (x − x)

ˆ x∈χ

R1 where, in both cases,

w ∼ CN(0, 1) and X denotes the set of constellation points of 2 -QAM. q The subset Xi = x = µ(b) : bq = i contains all constellation points whose corresponding bit vector has value i ∈ {0,1} in its q-th position. Note that while the CM capacity depends only on the QAM constellation, the BICM capacity is also a function of the bit labelling. For the values in Figure 5.2, Gray labelling has been assumed.

Modulation and Coding Techniques

8

Capacity [bits per channel use]

7

137

256−QAM

Unconstrained AWGN CM capacity BICM capacity

64−QAM

6 5 4

16−QAM

3 2

QPSK

1 0

BPSK

0

5

10

15 ES/N0 [dB]

20

25

30

Figure 5.2 BICM capacity in comparison with CM capacity and capacity of the unconstrained AWGN channel.

5.3 Coding Schemes This section presents two candidates for forward error correction (FEC) coding for medium and large packet lengths, which were chosen within the project: quasi-cyclic block low-density parity-check (QC-BLDPC, the FEC scheme for the WINNER reference design) codes in Section 5.3.1 and duo-binary turbo codes (DBTC) in Section 5.3.2. Both schemes yield a superior performance at packet lengths above 200 information bits and can be implemented efficiently. However, for smaller packets (e.g. those needed for broadcast control information), they are not applicable and a low-rate convolutional code (in Section 5.3.3) is proposed instead, for information lengths down to 25 information bits. The decoders for DBTC and QC-BLDPC codes are affected by several impairments of the overall system, the accuracy of channel estimation being the key parameter. Its influence on the decoding process is shown for the LDPC codes. These results can be used to assess the applicability of channel estimation algorithms. In Section 5.3.4, the choice of coding scheme for the system concept is justified and explained.

5.3.1 Low-density Parity-check Codes Among the increasing number of subsets of low-density parity-check (LDPC) codes, only a few are seen as serious candidates for next-generation wireless systems [LZ04, LR+06]. Indeed, for realistic future systems, many different constraints have to be taken into account simultaneously, such as performance, encoding and decoding complexity and decoder throughput (parallelism), resulting in what is called an ‘adequacy algorithm architecture’ approach

138


[Dor07]. One of the most promising candidates is the family of quasi-cyclic block low-density parity-check codes (QC-BLDPCC) [Fos04]. QC-BLDPC codes are defined by sparse parity-check matrices of size M × N consisting of square submatrices (subblocks) of size Z × Z that are either zero or contain a cyclic-shifted identity matrix. M is the number of rows in the parity-check matrix, N is the code-length (number of columns) and the information size K is given by K = N − M. These parity-check matrices are derived from the base matrix Hb of size m × n and the expansion factor Z , which determines the subblock size and hence the size of the derived code. From one base matrix, different code lengths can be constructed using different expansion factors: N = Z ·n

(5.2)

There is one base matrix specified per mother code rate: R = K /N = 1 − m/n

(5.3)

The entries of the base matrix are integer values defining the content of the subblocks: Hb = ( pi j ) 1≤i≤m b

(5.4)

1≤ j≤n

In the expansion process each entry pi j is replaced by a Z × Z square matrix that is:

r a zero matrix 0 Z ×Z , if pi j < 0 r an identity matrix I Z ×Z shifted to the right by pi j mod Z , if pi j ≥ 0. The base matrix always consists of a systematic part Hs and a parity part Hp : Hb = Hs |Hp

(5.5)

Consequently a codeword c consists of a systematic part s and a parity part p: c = [s|p] = [s1 s2 · · · s K | p1 p2 · · · p M ]

(5.6)

The parity part of the base matrix is in an approximate lower-triangular form (see Figure 5.3). Therefore, the parity-check matrix resulting from the expansion process is also partially

Figure 5.3 Parity part of the base matrix.


139

Figure 5.4 Structure of parity-check matrix after expansion.

lower-triangular and always has the structure introduced in [RSU01] (see Figure 5.4, in which the shaded area represents arbitrary sparse matrix entries). As reminder, this is equivalent to the notation introduced in [RU01] and shown in Figure 5.5 (with the initial notation, we have Z = p = g).

5.3.1.1 Encoding of BLDPC Codes Method 1 The parity-check matrix that was obtained from the expansion process has approximate lowertriangular form as depicted in Figure 5.4. Before encoding, the part of the matrix that is not lower-triangular, i.e. the last Z rows, have to be pre-processed. The pre-processing is done by Gaussian elimination and consists of the following steps: 1. The entries in the lower-right corner are eliminated to achieve the structure shown in Figure 5.6. Note that the area denoted by P is no longer sparse. 2. The last Z rows are processed to achieve an upper-triangular form as shown in Figure 5.7. The resulting parity-check matrix is denoted as H in the following discussion. 3. Determine the first Z parity bits by backward substitution using the last Z rows: pk =

N −M j=1

H M+1−k, j s j +

k−1

H M+1−k, j+N −M p j

k = 1, . . . , Z

(5.7)

j=1

Figure 5.5 Form of parity-check matrix, from [RU01]. (Reproduced by Permission of IEEE © 2009).

140


Figure 5.6 Structure of parity-check matrix after pre-processing step 1.

4. Determine the remaining (M − Z ) parity bits by forward substitution using the first (M−Z ) rows: pk+Z =

N −M

H k, j s j +

N −M+Z j=N −M+1

j=1

H k, j p j +

N −M+Z +k

H k, j p j

k = 1, . . . , M − Z

j=N −M+Z +1

(5.8) Method 2 The second method follows strictly the instructions given in [RSU01], by taking advantage of the structure of the parity-check matrix [MYK05]. Indeed, it can be demonstrated (see Figure 5.5) that:

I −ET−1

A 0 H= (−ET−1 A + C) I

B T (−ET−1 B + D) 0

(5.9)

Then, since all codewords have to be orthogonal to the parity-check matrix: HxT = 0

(5.10)

We end up with the following system of equations: ⎧ ⎨ AsT + Bp1T + Tp2T = 0 ⎩ (−ET−1 A + C)sT + (−ET−1 B + D)pT = 0 1

Figure 5.7 Structure of party-check matrix after pre-processing.

(5.11)


141

Figure 5.8 BLDPC code encoding pipeline structure.

Finally, by introducing the matrix = −ET−1 B + D, solutions of this system will give us the parity bits: T p1 = −−1 (−ET−1 A + C)sT (5.12) p2T = −T−1 (AsT + Bp1T ) This whole encoding process can be implemented in a high-throughput pipeline structure (see Figure 5.8). Thanks to the particular structure of the LDPC codes targeted within WINNER, we can take advantage of both the pipeline structure and reduced complexity, since operation (2) from Figure 5.8 is not needed. Indeed, as part of the joint design, we put specific constraints in order to end up with the identity matrix for the matrix. Operations (1) and (3) can easily be performed through simple back-substitution thanks to the double-diagonal structure of matrix T: ⎤ ⎡ IZ 0 K 0 ⎢ IZ IZ 0 K ⎥ ⎥ (5.13) T=⎢ ⎣0 0 K 0⎦ 0 K IZ IZ 5.3.1.2 Decoding Methods The standard algorithm for decoding LDPC codes is the ‘belief propagation algorithm’ (BPA) [MN96], of which several good approximations exist. This decoding algorithm computes the distribution of reliabilities of the Tanner graph by iteratively exchanging messages between the variable and check nodes. Tanner introduced an effective graphical representation for LDPC codes. The Tanner graph is equivalent to the structure of the parity-check matrix of the code. These graphs provide a complete representation and help to describe the decoding algorithm. Tanner graphs are bipartite graphs i.e. the nodes of the graph are separated into two distinctive sets and edges. There are two types of node: variable nodes (also called bit nodes) and check nodes. The way of switching between bit and check node updates is referred to scheduling; it will be discussed later on, as this can impact the decoder complexity. Decoding involves two major steps, the check node update and the bit node update. In Figure 5.9, intrinsic values from the channel feed first bit nodes (parents), then extrinsic

142


(a)

(b)

Figure 5.9 Belief propagation decoding of LDPC codes, from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

information is processed and forwarded to check nodes (children), that themselves produce new extrinsic information relying on parity-check constraints, feeding their connected bit nodes. The rules for updating the bit nodes are as follows: r extrinsic: Tn,m = In + E m ,n ; m ∈M(n)\m r a posteriori probability: Tn = In + E m ,n . m ∈M(n)

The rules for updating the check nodes are as follows: r sign processing: Sign(E n,m ) = Sign(Tn ,m ); n ∈N (m)\n x r magnitude: E n,m = Tn,m where (x) = − log tanh . 2 n ∈N (m)\n We have to introduce some notation:

r N (m): the set of bit nodes connected to the check node whose index is m. r M(n): the set of check nodes connected to the bit node whose index is n. In order to efficiently compute all messages, we need to follow the process involving some accumulated summation. The rules for updating the bit nodes with summation are as follows:

r extrinsic: Tn,m = Tn − E m,n ; r a posteriori probability: Tn = In + E m ,n . m ∈M(n)

The rules for updating the check nodes with summation are as follows: r sign processing: Sign(E n,m ) = Sign(Tn ,m ); n ∈N (m)\n r magnitude: Sm = Tn ,m , E n,m = Sm − Tn,m . n ∈N (m)


143

5

y

4

3

2

1

0 0

1

2

3 x

4

5

6

Figure 5.10 Shape of non-linear Phi(x) function.

The sub-optimal min-sum algorithm (MSA) has been proposed in literature in order to decrease the complexity by avoiding any call to a non-linear function. This is the most famous sub-optimal decoding algorithm. As we’ll see, its creation and functioning is quite similar to the max-log-map decoding algorithm used with duo-binary turbo codes. The keystone of such a low-complexity algorithm is considering the following approximation: (|Tn ,m |) ≈ (|Tn 0 ,m |) n ∈N (m)\n

where n 0 = Arg min Tn ,m . n ∈N (m)\n

This is quite understandable when we consider the shape of the non-linear function in Figure 5.10. be seen that the X values give the highest Y values. This means that the lower It can lowest Tn,m is, the higher Tn,m will be. By exploiting the property [ (x)] = x, we get

E n,m = min Tn ,m = Tn ,m . 0 n ∈N (m)\n

In [CF02], Fossorier explained that the reason the min-sum algorithm is so degraded compared with log-likelihood ratio belief propagation (LLR-BP) is due to an overestimation of the reliabilities. In short: BP Min−Sum . E < E n,m n,m This overestimation can be compensated for by means of two simple countermeasures:

r a scaling factor, α; r an offset, β.

144


One easy way to compensate for the overestimation is to apply a scaling factor in front of the LLRs: replace replace E n,m ← −−− α · E n,m and E n,m ←−−− α · Tn ,m 0

Another option is to subtract the amount of overestimation, then using the following new outgoing LLR: replace replace

E n,m ← −−− Max E n,m − β, 0 and E n,m ←−−− Max Tn 0 ,m − β, 0 The MAX operator ensures that all outgoing LLR whose magnitude is below the given threshold, β, are not considered. We can combine the counteracting efforts (i.e. we can use both α and β) by applying the following new updating rule: replace

E n,m ← −−− Max α · E n,m − β, 0 and replace

E n,m ← −−− Max α · Tn 0 ,m − β, 0

5.3.1.3 Scheduling Algorithms In the standard schedule of the BPA, which is often called the flooding schedule, all check nodes are updated first and then all variable nodes are updated. An alternative is ‘shuffled scheduling’ [Man02], also known as layered decoding, in which the BP decoder uses not only the messages from the last iteration but also information about the updates from the current iteration. This leads to a considerable increase in convergence speed. We can distinguish between horizontal and vertical shuffling. The vertical-shuffling schedule operates along the variable nodes: all check nodes connected to the current variable node are updated and the current variable node is updated. The horizontal-shuffling schedule operates along the check nodes: the current check node and all the variable nodes connected this check node are updated. As already emphasized, there are no theoretical constraints concerning the sequence of updating nodes. That means performance should be the same independent of the scheduling order. This is particularly true for graphs without cycles. Nevertheless, we can still observe some slight differences for codes whose graphs have some cycles. The main idea of finding smart-scheduling algorithms is to allow fast propagation of updating messages through the graph. As we’ll see, choosing the scheduling algorithm properly can result in twice as fast a speed of convergence. Flooding Scheduling This is the initial, or reference algorithm, where all check nodes, then all bit nodes are updated (Figure 5.11). Smarter scheduling algorithms can be implemented, resulting in faster convergence speed.


145

Figure 5.11 Flooding scheduling process.

Horizontal Scheduling The horizontal scheduling algorithm (Figure 5.12) was proposed by Mansour [Man02]. In this case, we treat one check node after the other then, for each of them, we update their corresponding bit nodes N (m). Vertical Scheduling This scheduling algorithm (Figure 5.13), which is similar to the previous one, was proposed by Fossorier [ZF02]. Performance Assessment Comparing the performance of different scheduling algorithms has mainly focused on the convergence speed (see Figure 5.14). From these results, we can deduce that the convergence speed can be at least doubled by means of a smart scheduling algorithm. 5.3.1.4 Lifting Process of LDPC Codes In this section, we deal with new requirements from the WINNER system concept, ending up with a codeword length above 27 000 bits.

Figure 5.12 Horizontal scheduling process.

146


Figure 5.13 Vertical scheduling process.

In order to ensure not only consistency, but backward compatibility with BLDPC codes developed under WINNER I (Rc = 1/2, 2/3, 3/4) and with the lowest coding rate Rc = 1/3, we will use the well-known ‘lifting’ method on our former parity-check matrices. As demonstrated in [MN96] and [MYK05], applying ‘lifting’ to existing LDPC codes, enables us to increase the maximum allowable codeword length, whilst keeping the same performance for the previous range of codeword lengths (backward compatibility). Our current constraints are as follows:

r Nb = 48: the codeword length is a multiple of 48 (compare with the dimension of base-model matrix);

r maximum codeword length is 96 ∗ 48 = 4608; r maximum expansion factor is Zfmax = 96. The new values are:

r maximum codeword length is 576 ∗ 48 = 27648; r maximum expansion factor is Zfmax = 576. With the notation introduced in [MN96], the resulting exponents E(Hk ) of the parity check matrix Hk corresponding to expansion factor L k are given by: E(Hk ) = E(H) mod (L k )

(5.14)

By applying step by step the modulo-lifting procedure described in [MN96], we have thus produced new parity-check matrices for the following coding rates: Rc = 1/3, 1/2, 2/3, and 3/4, leading to the performances shown in Figure 5.15. 5.3.1.5 Rate-Compatible Puncturing Codes Block LDPC codes are quasi-cyclic, i.e., a cyclic-shift by a number smaller than the subblock size Z of a codeword yields another codeword. From the symmetry of the codes, it follows that each bit within one subblock is equally important for the decoder and, hence, equally

147

Average Number of Iterations

100

101

102

1.6

1.8 (a)

2 2.2 Eb/No (dB)

2.4

2.6

2.8

0.8

1

1.2

1.4

1.6

1.8

2

2.2

1.4

1.6

1.8

2 2.2 Eb/No (dB) (b)

Horizontal Scheduling Vertical Scheduling

2.4

2.6

CV Speed Scheduling, LLR-BP, AWGN, BPSK

Figure 5.14 Convergence speed comparison: (a) absolute number of iterations and (b) ratio of number of iterations.

1.4

Horizontal Vertical Flooding

Convergence Speed Scheduling, LLR-BP, AWGN, BPSK

Ratio # iterations = CV Speed Gain

2.8

148 Figure 5.15 CWER performance results with lifted LDPC codes.


149

suitable for puncturing. It is therefore reasonable to define the puncturing pattern as being ‘subblockwise’. For the R = 1/2 base matrix (which can be found on this book’s companion website), a ≤ R ≤ 24 . All these set of puncturing patterns was optimized for the code-rates in region 24 26 48 puncturing patterns are described by the priority vector P: P = [1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 30, 34, 38, 41, 42, . . . 13, 29, 46, 8, 32, 25, 37, 40, 44, 48, 27, 45, 33, 35, 36, 47, 31, 28, 26, 39, 9, 43, 22] (5.15) The priority vector P gives the order in which subblocks of the codeword should be sent in an H-ARQ process. It can be used to define an interleaver in order to implement arbitrary punctured code-rates elegantly. The currently available set of modulation and coding schemes (MCS) for rate-compatible punctured (RCP) BLDPC codes is limited to the combination of the following parameters: b = {1, 2, 4, 6, 8} R=

24 , 48 − P

P = 0, 2, 4, . . . , 22

(5.16) (5.17)

where b = log2 (M) is the number of bits per constellation symbol (M is the constellation size), R is the code rate, and P is the number of punctured subblocks from the codeword for mother code rate R = 1/2. The simulation results1 presented in Figure 5.16 were obtained through the Monte Carlo simulation using the following simulation chain:

r In the transmitter, each information packet of K = 288 or K = 1152 random bits2 has been encoded with the BLDPCC encoder, then rate-compatible punctured, interleaved using a pseudo-random bit interleaver and finally mapped into constellation symbols of b bits. Such a block of symbols has been transmitted through an AWGN channel. r In the receiver, a soft demodulation has been performed for each symbol of a block to obtain log-likelihood ratios (LLR). The demodulator assumed max-log-MAP approximation. The LLR block is deinterleaved, depunctured and sent to the BLDPC code decoder. The decoder employs a standard belief propagation algorithm in the LLR domain in parallel fashion (flooding schedule), i.e. all variable→check node messages are updated in one sweep and all check→variable node messages are updated in another sweep. The maximum number of decoding iterations has been set to 50.

5.3.1.6 SNR Mismatch Impact on LDPC Codes Whilst evaluating performance of advanced coding techniques, namely iterative coding such as turbo codes and LDPC codes, it is necessary to take into account multiple impairments resulting from the system in which such coding techniques are used. 1 The

database with the RCP BLDPC code BER and CWER performance results in the form of text files and plots can be found on the WINNER project web pages. 2 These two sizes of packet are taken from the baseline design assumptions.

150


RCP BLDPC code; R = 24/48-P); K = 288; QPSK; AWGN channel P=0 P=2 P=4 P=6 P=8 P = 10 P = 12 P = 14 P = 16 P = 18 P = 20 P = 22

1.0e+00

CWER

1.0e-01

1.0e-02

1.0e-03

1.0e-04

1

2

3

4

5 Eb/No [dB]

6

7

8

9

Figure 5.16 CWER curves for QPSK (and BPSK) and K = 288.

As a result, although optimal decoding algorithms (such as log-MAP for turbo codes or LLRBP for LDPC codes) enable performance close to Shannon Capacity, they might experience severe degradation due to external impairments. One of the key parameters common to both decoders is the SNR estimation [SW98, Kha03, SBH05]. Therefore it is mandatory to evaluate the accuracy requested by SNR estimation algorithms (impacted by channel estimation) in order to avoid prohibitive performance degradations. In this section, we restrict ourselves to considering only LDPC codes. In order to obtain sufficient valuable and relevant results, various modulations have been taken into account: QPSK (see Figure 5.17), 16-QAM and 64-QAM, with a half-rate Rc = 1/2 LDPC codes, as defined in [WIN1D210]. Further detailed results can be found in [WIN2D223]. Depending on the acceptable degradation in performance (BER or CWER), these curves can be used for checking the suitability of channel-estimation algorithms through their impact on the SNR estimation. For instance, with QPSK for an operating point of E b /N0 = 3 dB, the SNR offset can be in the range [−3; +3] dB, if we want to avoid a BER above 10−5 . It’s worth noting that an offset of −5 dB (underestimation) will force such QPSK transmission (true E b /N0 = 3 dB) to be degraded up to a BER close to 0.1. On the contrary, even after +10 dB offset (overestimation), we are still around BER = 10−2 . We conclude that, even though the log-BP decoding of LDPC codes is optimal in terms of performance, it might lose this advantage due to mismatched SNR estimation. The sensitivity of such a decoding algorithm is more robust to overestimation than underestimation.


151

Figure 5.17 SNR mismatch impact on LDPC codes, R = 1/2, QPSK.

5.3.2 Duo-Binary Turbo Codes The block diagram in Figure 5.18 shows the structure of the turbo encoder and decoder, which consists of a parallel concatenated convolutional encoder and the corresponding iterative decoder. The information message u is encoded twice: directly by the encoder C1 and a permuted version of the message by the encoder C2 . Both encoded bitstreams as well as the message itself are transmitted. At the receiver side, each coded bitstream is decoded separately by a soft-in soft-out decoder and the obtained information is used by the other decoder, which in turn returns new extrinsic information to the first decoder. After several iterations, the a posteriori L-values obtained are mapped to an estimate of the message u by hard decoding. In Figure 5.18, the received channel symbols are scaled appropriately and are demultiplexed into the L-values corresponding to the systematic bits, L u , those corresponding to the coded bits of encoder C1 , L c1 , and the L-values associated with the encoded bits of C2 , L c2 . E 1 denotes the extrinsic information of the first decoder, which becomes the a priori information A2 for the second decoder and vice versa for E 2 and A1 . Duo-binary turbo codes are used in several standards, e.g. [ETSI02, IEEE16e04], and have been found to offer very good performance in conjunction with higher-order modulation [BJD01]. The main enhancement from DBTC with respect to the original turbo codes lies in the component codes, which encode two bits at a time. As usual for parallel turbo codes, both

152


Figure 5.18 Block diagram of a parallel concatenated turbo coding scheme.

component codes are identical. The term ‘duo-binary’ is somewhat misleading since the component codes are still binary convolutional codes (only the number of input bits per transition is k0 = 2) and all operations are carried out in the binary field GF(2). The transfer function matrix of the component encoders is given by ⎛ ⎜1 G(D) = ⎜ ⎝

0

0

1

1 + D2 + D3 1 + D + D3 1 + D + D2 + D3 1 + D + D3

⎞ 1 + D3 1 + D + D3 ⎟ ⎟ 1 + D2 ⎠ 1 + D + D3

(5.18)

As in all parallel turbo codes, the component codes are recursive. One of the salient features of turbo codes is that the component codes are relatively simple codes with low memory. This is also true here since the component codes defined by G(D) have only S = 8 states. The turbo encoder comprises two component encoders (see Figure 5.18); thus the mother code rate of the turbo encoder is 1/3, since for each input bit couple (u (1) , u (2) ) that is transmitted, two encoded bit couples (c1(1) , c1(2) ), (c2(1) , c2(2) ) are produced.

5.3.3 Low-Rate Convolutional Codes for Control Channel The modulation and coding requirements for control channel signalling are different from the ones for user data transmission. The information sent through the control channel is very important for proper functioning of the advanced protocols of the WINNER concept. Although the proposed BLDPCC and DBTC provide excellent coding performance as shown in [WIN1D210], they can’t be used for encoding the control information due to the very short packet sizes (25 information bits) being considered. Therefore low-rate convolutional codes, which can be used for encoding such short packets by choosing a tail-biting algorithm, are still considered for the WINNER reference design. The BER and CWER performance results presented in Figures 5.19 and 5.20 have been obtained for the convolutional code with the following generator polynomials: G B = [473, 513, 671, 765]oct . These results are compared with the results for the convolutional code


153

Convolutional codes performance (BPSK; AWGN; tail biting; k = 25 inf. bits; GA = [575, 623, 727]oct; GB = [473, 513, 671, 765]oct) 1.0e+00

CWER, RB = 1/2, P = [3,0,0,3] CWER, RA = 1/3 CWER, RB = 1/4 BER, RB = 1/2, P = [3,0,0,3] BER, RA = 1/3 BER, RB = 1/4

BER / CWER

1.0e-01

1.0e-02

1.0e-03

1.0e-04 -8

-7

-6

-5

-4

-3 SNR [dB]

-2

-1

0

1

2

Figure 5.19 BER and CWER vs SNR results of R = 1/4 (ODS), R = 1/3 (MFD) and R = 1/2 (ODS, punctured) convolutional codes for K = 25 information bits (BPSK, AWGN, tail biting).

from WINNER Phase I. Additionally, R = 1/2 results have been obtained from the same mother convolutional code using the puncturing matrix from Equation (5.19). ⎡ ⎤ 1 1 ⎢0 0⎥ ⎥ P=⎢ (5.19) ⎣0 0⎦ 1 1 The complexity of the tail-biting Viterbi decoding needs to be taken into account. The ‘bruteforce’ tail-biting algorithm is 2k(L−1) times more complex than a standard Viterbi decoding with a known tail, where k represents the number of inputs of the convolutional code (R = k/n) and L is the constraint length. For a convolutional code with L = 9, this means an additional complexity factor of 256. Therefore other convolutional codes with shorter constraint lengths seem to be a good compromise between the decoding complexity and performance figures. Figure 5.20 compares CWER and BER results of a few R = 1/4 ODS convolutional codes with different constraint lengths, i.e. L = {6, 7, 8, 9}.3 The CWER performance of the shortest code in this group, i.e. with constraint length L = 6 is about 0.5 dB worse than the code with L = 9. On the other hand, the decoding complexity of this shortest code is 29–6 = 8 times lower than the longest one. following generator polynomials have been used for R = 1/4 convolutional codes: G 6 = [51, 55, 67, 77]oct , G 7 = [117, 127, 155, 171]oct , G 7 = [231, 273, 327, 375]oct , and G 7 = [473, 513, 671, 765]oct . All of them are optimum distance spectrum (ODS) convolutional codes [FOO+98]. 3 The

154


Convolutional codes performance (BPSK; AWGN; tail biting; k = 25 inf. bits) 1.0e+00 CWER, R = 1/4, L = 6 CWER, R = 1/4, L = 7 CWER, R = 1/4, L = 8 CWER, R = 1/4, L = 9 CWER, R = 1/3, L = 9 BER, R = 1/4, L = 6 BER, R = 1/4, L = 7 BER, R = 1/4, L = 8 BER, R = 1/4, L = 9 BER, R = 1/3, L = 9

BER / CWER

1.0e-01

1.0e-02

1.0e-03

1.0e-04 -8

-7

-6

-5

-4

-3

-2

-1

0

SNR [dB]

Figure 5.20 BER and CWER vs SNR results of R = 1/4 ODS convolutional codes for various constraint lengths L and K = 25 information bits (BPSK, AWGN, tail biting).

5.3.4 Comparison of Coding Schemes 5.3.4.1 Performance Comparison The performance comparison (Figure 5.21) underlines the fact that the selection of an appropriate coding technique depends crucially on the target block length. For a code rate of 0.5, DBTC outperforms BLDPCC for block lengths up to 1728 (0.2 dB gain over BLDPCC for N = 576). Then BLDPCC starts progressively to outperform DBTC (0.1 dB better for N = 4308). In general, the performance loss by going from large to small block sizes is lower for DBTC than for BLDPCC. The threshold (in terms of block length) that separates these two regimes, however, depends on the code rate. When increasing the code rate to Rc = 3/4, a block length of 1152 is sufficient for the BDLPCC to achieve the same performance as the DBTC, and the difference observed for N = 576 is very small (see Figure 5.23). 5.3.4.2 Performance–Complexity Trade-Off Relying on the computational complexity assessment introduced in [WIN1D23] and [3GPP05], together with the relative cost of operations given by [WIN1D23, Table 5.1], the energy consumption and cycle counts were computed for both DBTC and BLDPCC. For the sake of clarity, we restricted the presentation of results to two extreme block length cases N = 2304 and N = 576. In the case of DBTC, only the max-log-MAP decoder with scaling of extrinsic


155

Figure 5.21 Performance comparison between DBTC and QC-BLDPCC, Rc = 1/2, reproduced from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

information (factor 0.5 in the first iteration, 0.75 in the second to seventh iterations and 1 in the last iteration) is used, as it has been proven to give performance similar to log-MAP decoding, with an attractive reduction in complexity. Similarly, the MinSum∗ algorithm (MSA∗ ) provides the best trade-off for LDPC decoding. Additionally, alternative schedulings (shuffled decoding) Table 5.1 Assessment of the most promising FEC techniques.

b

Performance (dB)

Short blocks Medium blocks Large blocks Memory requirements Code structure Decoding Encoding complexity (operations per information bit) Decoding complexity (operations per information bit) Maturity a

CCa

DBTC

BLDPCC

– 1. The first observation is that the integral cannot be solved analytically, but a numerical approximation is sufficient for this analysis. Using the model introduced here, different types of RAPs, both wired base stations and relays, can be evaluated following the same procedure, but with different assumptions regarding the antenna gains and output power. Resources in Relay-Enhanced Cells The previous section has provided a method of determining if a RAP can handle the offered traffic in its cell, from a radio resource perspective. It was general for any type of RAP, and is therefore valid also for relay nodes (RNs). However, deploying an RN is different in one important aspect compared to other types of RAPs, namely that the RN requires a feeding RAP since it is not connected to a backhaul transmission network. Hence, when deploying an RN, consideration has to be taken not only of the RN itself, but also the resource situation of the serving RAP, from here on referred to as a BS. In order for an RN deployment to be beneficial in general, the BS to RN link should be more favourable from a path-loss perspective than the BS to UT link. In the propagation model used, line of sight (LOS) transmissions are assumed between the RNs and the feeding BS while other transmissions between RAP and UT handle non-LOS conditions. Further, the propagation between a BS and the RNs served by other BSs, are also assumed to follow nonLOS conditions. Now, resource control for an RN-based deployment involves three parameters: ρ BS controls the resources spent on BS–UT transmissions; ρ RN , for the RNs that are served by the BS in question, controls the resources spent on RN–UT transmissions; ρ BS,RN controls the (LOS) transmissions between the BS and the RNs. To guarantee that a relay-enhanced cell can serve its users, the following two constraints must be satisfied: ρBS + ρBS,RN ≤ 1

and

ρRN + ρBS,RN ≤ 1

(14.9)

The ρ BS,RN is specific per BS cell, i.e. each cell includes a phase where only BS to RN transmissions are performed. It is clear that the length of this phase is shorter if the BS to RN transmissions are favourable, i.e. LOS, and longer if the BS to RN conditions are unfavourable. Equations (14.9) secure that both the BS and the RN can serve its own users and at the same time also manage to transmit (BS) and receive (RN) traffic on the LOS link between BS and RN. Scheduling and Interference Model Introducing a heterogeneous traffic model has the consequence that different BS cells will have different magnitudes of traffic to serve, making ρ BS differ significantly between BSs. This in turn implies that an interference model based on all other RAPs transmitting with equal power all the time is no longer applicable, and the need for some kind of scheduling mechanism is called upon. Therefore, we divide the snapshot into a number (e.g. 100) of time

Cost Assessment and Optimisation for WINNER Deployments

549

frames. Now, if a BS has a ρ BS equal to 0.5, based on all RAPs transmitting with equal power all the time, it will on average transmit only in 50 out of the 100 frames, whereas a BS that is capacity-limited will transmit in all of the frames. It seems reasonable to account for this in the interference model. The approach here is that, for every frame, a random number between 0 and 1 will determine if a certain BS is to transmit or not, by comparing the random number to ρ BS for the BS in question. If the random number is lower than ρ BS , this implies that the BS will transmit in that particular frame. This enables a new average rate to be calculated between each BS and its users, based on the different rates achieved in the phases of active transmission. Since the interference is reduced, the new rate is higher, which in turn results in a lower ρ for all RAPs. The problem is that since ρ has changed, so have the interference levels based on ρ and it is clear that the process is iterative. To reach a stable ρ (level of interference), we therefore perform consecutive recalculations, until the value has converged. In the simulations, 100 frames and five iterations are used, since this has been shown to give reliable results. Note that deploying an additional RAP in the network requires that the above procedure must be carried out to adapt both the new RAP measure and the already deployed ones to the new conditions.

14.4.4 Cost Calculation One possibility for evaluating the deployment cost is to estimate the total cost related to every type of RAP, including the costs for equipment, site acquisition, site rental, transmission, power, operation and maintenance, etc. (see discussion in Section 14.3). Thereafter, the total deployment cost can be determined by considering only the number of different RAPs. An approach based on the assumption that the total deployment cost scales close to linearly with the number of RAPs is frequently used in related research on the subject [JFK+04], [Zan97], but should always be checked with respect to the chosen deployment environment. The main advantage with the methodology described in this chapter is its general applicability to all scenarios where the mentioned assumptions apply. With necessary model parameters, regarding cost values as well as radio model assumptions, many different RAPs and combinations can be included in the deployment procedure. This implies that the WINNER system deployment cost not only can be analysed individually, but also compared to other systems. Further, it should be pointed out that the mean traffic density can be varied, which implies that we can study rural as well as metropolitan and high traffic density deployment areas.

14.4.4.1 Relay-Specific Cost Evaluation Issues An important aspect of the cost study is to compare a relay-based deployment (comprising macro and perhaps micro base stations together with RNs) to a benchmark system (using a macro base station single access but also the more interesting alternative including macro, micro and pico base stations, all connected to the wired infrastructure). Relay-based deployment has one main cost benefit compared to a wired BS with equal performance properties, namely that it is not associated with a transmission cost, i.e. the cost of a fixed line connecting the site to the backhaul IP network. The trade-off for the decrease in transmission cost is based on the fact that the traffic covered by an RN has to be transmitted

550


via the feeding base station, implying a decrease in the BS’s maximum capacity. If the BS is not fully loaded, excess capacity can be used to serve the associated RNs. From the model description above, it is clear that a relay-based deployment benefits from a large improvement in signal conditions between the BS and RN, i.e. typically α is significantly lower for line of sight compared to presumably shadowed transmissions. If the same α was valid for both LOS and other transmissions, a relay-based deployment could only be favourable in special coverage-enhancing situations for shadowed areas, since the feeding of the RN would then require almost as much resource as supporting the user on a single-hop link, making a twohop transmission less advantageous. Providing coverage to shadowed areas, i.e. covering spots where the signal conditions are very unfavourable, but not due to distance path loss, is not the focus of this analysis, and for such specific situations a comparison between RNs and repeaters is probably more appropriate. The outcome of the approach introduced here only concerns the other two WINNER relay-based deployment strategies: capacity and coverage-enhancing deployments, further explained in e.g. [WIN2D351]. 14.4.4.2 Deployment Representation by Indifference Maps Using the deployment model introduced in this chapter, the output provides information regarding the positions and numbers of necessary RAPs to cover the traffic in a predefined area. Estimating the total cost per RAP then makes it possible to calculate the total deployment cost. The optimum deployment densities of different types of RAP can also be represented by a theoretical framework. The deployment strategy of the modelling tool described above should closely match the mathematical optimum. A proposed mathematical cost analysis framework has been developed in WINNER II and was mostly designed for a cost comparison between a regular (single access) and a relay-based deployment, similarly to earlier cost evaluation work in the literature, see e.g. [Tim05]. The following sections review the cost analysis framework originally developed for the cost assessment of relay-enhanced cells and its extension to include different types of RAP. Note that this cost analysis framework provides optimum deployment densities for different RAPs, but no specific RAP locations are calculated. The introduced deployment model, on the other hand, tries to match these optimum densities by explicitly placing RAPs in a traffic density map. This placement procedure imposes certain restrictions on the deployment results regarding mathematical optimality. Furthermore, the methodology relies on the assumption that the mean cost per RAP type is constant, irrespective of deployment densities and locations, which might not be valid in certain areas (e.g., developing countries where the necessary infrastructure may not be available in significant parts of the deployment area). Indifference Map for Two Types of RAP (Base Stations and Relay Nodes) The performance of a multi-hop network, in terms of coverage and capacity density (total information rate per unit geographical area), improves with both RN density (the number of RNs per unit geographical area) and BS density (the number of BSs per unit geographical area). As a consequence, a decrease in BS density may be compensated for by an increase in RN density, in order to maintain constant performance. This trade-off is illustrated in the form of an indifference map in Figure 14.4, where an iso-performance or indifference curve is plotted. An iso-performance (indifference) curve is hence characterised by the fact that each


551

Figure 14.4 An indifference map showing the trade-off between BS density and RN density required to maintain constant capacity density and coverage.

point on the line represents a different multi-hop system, each having identical performance (see e.g. [Tim05] and [WIN2D351]). In Figure 14.4, one such system is shown having BS density B and RN density A. A tangent is also shown at this point on the indifference curve. Its gradient represents the change in RN density required to compensate for a change in BS density for this system. If the gradient of the tangent is equal to –r, where r is the ratio of the cost of a BS and the cost of an RN, then the tangent is an ‘equal-cost’ line, such that any point on the line represents the same total cost of BSs plus RNs. Then, r=

B

BS cost A = . −B RN cost

(14.10)

The significance of the system represented by the point at which the equal-cost line is tangent to the indifference curve is that it is the least-cost combination of BSs and RNs capable of providing this performance with this BS–RN cost ratio. If the BS–RN cost ratio were to vary, the corresponding equal-cost line would be tangent to the indifference curve at a different point, corresponding to a system with a different combination of BSs and RNs. The cost benefit of the multi-hop system relative to the conventional cellular system can also be obtained from the indifference curve: Cost benefit =

B0 × BS cost B0 B0 = = . B × BS cost + A × RN cost B + A/r B

(14.11)

Inspection of the indifference map in Figure 14.4 shows that the cost benefit increases with the BS–RN cost ratio, r, and with the curvature of the indifference curve. Note that the cost ratio r is a very important parameter and has to be carefully verified. For a network operator, it is also desirable to minimise the total amount of spectrum used by the system to deliver a specified service. This is equivalent to maximising the spectral efficiency of the system, which is defined as the ratio of the system capacity to the total amount of spectrum used by the system; that is, the system capacity per unit bandwidth. For the purposes of comparing conventional and relay-based systems, however, it is more useful

552


to define ‘effective spectral efficiency’ ηeff . Both spectral efficiency and effective spectral efficiency have units of bps/Hz/site.1 The recommended metric for the cost-efficiency comparison of different multi-hop systems is the effective spectral efficiency for a specified service at a particular level of coverage. It is described mathematically as follows: ηeff =

C(m) , S (1 + m /r )

(14.12)

where S = total spectrum used by the system, C(m) = capacity or total information rate per BS, including its RNs, m = A/B = ratio of the number of RNs to number of BSs in the network, and r = ratio of the cost of a BS and the cost of an RN In the absence of RNs (m = 0), this metric becomes equal to spectral efficiency, as used for conventional cellular systems, and is therefore equally applicable to multi-hop and conventional cellular systems. The cost benefit of a multi-hop system relative to a conventional cellular system is readily derived using the combined metric: 1/Cost benefit = With

B × BS cost + A × RN cost A × RN cost B + = . B0 × BS cost B0 B0 × BS cost

B C(0) = for equal capacity per unit area in both points, B0 C(m) 1/Cost benefit =

C(0) + C(m)

ηeff (0) C(0) A = (1 + m/r ) = C(m) C(m) ηeff (m) B× ×r C(0)

⇔ Cost benefit =

ηeff (m) B0 = . B ηeff (0)

(14.13)

Here, ηeff (0) represents the conventional cellular system with no RNs and ηeff (m) is the metric for the multi-hop system with m RNs per base station. Cost benefit > 1 if the relaybased network provides comparable spectral efficiency for a lower infrastructure cost. Indifference Map for Three or More Types of RAP The representation of optimum deployment by indifference maps can be extended to three or more dimensions (i.e., three or more types of RAP, such as macro BSs and two types of relay node). We illustrate the extension to three dimensions explicitly via the three-dimensional indifference map. The addition of more dimensions becomes obvious with the extension of the relevant equations. The trade-off between deployment densities of macro base stations (b), Type 1 relay nodes (a), and Type 2 relay nodes (c) is illustrated in Figure 14.5 by a three-dimensional indifference map in the positive subspace {a ≥ 0 ∩ b ≥ 0 ∩ c ≥ 0}. The convex iso-performance that for m = 0, the term ηeff does not express the spectral efficiency (cell capacity divided by radio bandwidth) but serves as a derived metric to compare different BS–RN deployments regarding cost effectiveness. These comparisons are only valid within a specific radio configuration (reuse factor, SDMA deployment, etc.). 1 Note


553

a

A’ ~–rba ~–rca

A

~–rca da

db

C/rbc B

A/rba B1

B’

B0

b

C C’

~–rbc

c

Figure 14.5 Indifference map for the three-dimensional case, from [W+08]. (Reproduced by Permission of IEEE © 2009).

or indifference surface curve is indicated by its intersections with the planes a = 0 and c = 0. Each point on the indifference surface now represents different deployment densities of the three RAP types, which exhibit identical performance. The optimum deployment is given by the BS density B, RN1 density A and RN2 density C. Note that the surface does not intersect with the plane b = 0 because neither RN1 nor RN2 nodes can operate without a supporting BS.2 The tangent ‘equal-cost’ plane at the point (A,B,C) of the convex indifference surface exhibits gradients representing the change in RN1 or RN2 density required to compensate for a change in BS density for this system. The A/B gradient of the tangent plane is equal to −rba and the C/B gradient is equal to −rbc , where rba and rbc are the ratios of the BS cost to the RN1 cost and RN2 cost, respectively, and any point on the plane represents the same total cost of BSs plus RN1s plus RN2s. Then, rba =

B

A BS cost = − B1 RN1 cost

and rbc =

B

C BS cost = . − B1 RN2 cost

(14.14)

The cost benefit of the multi-RAP-type system relative to the conventional cellular system with only one type of RAP can now be obtained from the indifference map, similar to the 2 The cost-optimal deployment representation by indifference maps can also be applied to other types of RAP, e.g. micro or pico BS within a mixed deployment, i.e. as long as there is at least one reference (macro) BS in the deployment. This constraint is necessary in the derivation of the cost benefit as a function of the effective spectral efficiency.

554


two-dimensional case: Cost benefit =

B0 × BS cost A × RN1 cost + B × BS cost + C × RN2 cost

=

A/rba

B0 B0 = + B + C/rbc B

(14.15)

The relation B = A/rba + B + C/rbc can also be geometrically derived from Figure 14.5: B − B1 = A/rba and B1 − B = db = da/rba = C/rbc because da/db = rba and da/C = rca with rca = rba /rbc . The cost benefit can be described in terms of the effective spectral efficiency in a similar way as for the two-dimensional case. The definition of ηeff is adapted accordingly: ηeff =

C(m, n) , S(1 + m/rba + n/rbc )

(14.16)

where S = total spectrum used by the system; C(m, n) = total information rate per BS, including its associated RNs (RN1 and RN2); m = A/B = ratio of the number of RN1s to the number of BSs in the network; n = C/B = ratio of the number of RN2s to the number of BSs in the network; rba = ratio of the cost of a BS to the cost of an RN1; rbc = ratio of the cost of a BS to the cost of an RN2. The cost benefit of a three-dimensional optimum deployment relative to a conventional cellular system is described using the combined metric: 1/Cost benefit = = With

B × BS cost + A × RN1 cost + C × RN2 cost B0 × BS cost B A × RN1 cost C × RN2 cost + + . B0 B0 × BS cost B0 × BS cost

C(0, 0) B = for equal capacity per unit area in both points, B0 C(m, n) C A + C(m, n) C(m, n) B× B× × rba × rbc C(0, 0) C(0, 0) ηeff (0, 0) C(0, 0) = (1 + m/rba + n/rbc ) = C(m, n) ηeff (m, n)

1/Cost benefit =

C(0, 0) + C(m, n)

⇔ Cost benefit =

ηeff (m, n) B0 = B ηeff (0, 0)

(14.17)

Here, ηeff (0, 0) represents the conventional cellular system with no RNs and ηeff (m, n) is the metric for the multi-hop system with m RNs of type 1 and n RNs of type 2 per base station. The extension from the two-dimensional to the three-dimensional case can be used similarly to describe deployments with even more dimensions (types of RAP). The equations in the Ndimensional case can be derived by the same extension method as described before.


555

14.5 Reference Deployment Scenarios and Cost Assessments In this section, deployment simulation results are presented for different example scenarios. Based on these results, cost optimisations are performed regarding the deployment densities of different types of RAP. Furthermore, some complementary cost assessments are given for MIMO base station deployment scenarios that are not covered by deployment simulations. The results in this section should be regarded as examples that illustrate the cost optimisation procedure based on iso-performance curves. The presented cost assessment results depend strongly on the assumptions about cost figures, deployment strategy, and scenario. It is not the intention of this section to provide general statements about the cost efficiency of certain deployment options. The specific gains of certain deployment options should always be assessed after the underlying assumptions have been carefully determined. It should be strongly emphasised that the results in this section are only valid in the example scenario and for the related assumptions explained in the text.

14.5.1 Deployment Simulations and Assumptions Deployment simulations were carried out for two scenarios that used a channel model of B5a LOS for the BS–RN link:

r S1 is WA urban, FDD, 3.95 GHz, 2 × 50 MHz with a channel model of C2 NLOS for the BS–UT link;

r S2 is WA rural, FDD, 2 GHz, 2 × 10 MHz with a channel model of D1 NLOS for the BS–UT link. The simulations used two deployment strategies:

r D1 models a WA greenfield operator with non-uniform flexible (according to traffic) deployment of a macro BS;

r D2 models a WA incumbent operator with fixed hexagonal positions for the macro BS. Both strategies used intelligent deployment of other RAPs according to traffic. Table 14.7 contains a list of the case studies for which cost assessments are presented in the remainder of this section. Table 14.8 lists the simulation parameters that depend on the scenario and Table 14.9 contains the parameters that depend on the RAP type.

14.5.2 Case Studies 1 and 2: WA Urban, Relay Nodes vs Micro BS 14.5.2.1 Simulation Scenario Description The results in this section are based on eight macro BS radii and a network area of 20 × 20 km. Parameter values are found in Section 14.5.1. Further, the values in Table 14.10 are obtained as the mean values of 10 simulations based on randomly generated traffic maps. Another important parameter is the cell radius for the RNs and micro BSs. The choice of 300 m here guarantees that the RN and micro BS cells are not capacity limited, since that could imply that not all the users are supported.

556


Table 14.7 Case studies for cost assessments. Case study

Scenario

Deployment strategy

1 2

S1 S1

3 4

RAP types

Assessment subject

D2 D2

Macro BS, RN Macro BS, micro BS

Gain of RNs compared to micro BSs, urban, hexagonal macro BS position

S2 S2

D2 D2


Gain of RNs compared to Micro BSs, rural, hexagonal macro BS position

5 6

S1 S1

D1 D1


Gain of RNs compared to Micro BSs, urban, nonuniform macro BS position

7 8

S1 S1 MIMO

D1 D1

Macro BS Macro BS with MIMO

Cost gain due to sparser MIMO deployment

It is clear that when the macro BS density decreases, the number of RNs or micro BSs increases. This is expected, since more macro BS cells become capacity limited. Further, and also in line with the discussion in Section 14.4.3.4, the number of RNs always exceeds the number of micro BSs and the difference seems to be stable at around 10 %. Figure 14.6 shows an indifference map for a deployment consisting of macro base stations with RNs or micro BSs. The curves follow each other closely, but it is clear that the relay-based deployment requires a higher density of RAPs compared to the micro BS deployment. Table 14.8 Scenario-dependent simulation parameters. Parameter

S1 (urban)

S2 (rural)

3.95 2 × 50 1

2 2 × 10 1

BS–RN link Percentage of NLOS/LOS conditions Path loss : offset β Attenuation exponent alpha per link type Noise power spectral density (dBm/Hz) Noise figure (RN receiver) (dB)

100 % LOS B5a 40.5 2.35 −174 5

100 % LOS B5a 34.5 2.35 −174 5

BS–UT or RN–UT link Percentage of NLOS/LOS conditions Path loss: offset β Attenuation exponent alpha per link type Noise power spectral density (dBm/Hz) Noise figure (UT receiver) (dB)

100 % NLOS C2 40.5 3.57 −174 7

100 % NLOS D1 46.9 2.52 −174 7

Radio Carrier frequency (GHz) Radio bandwidth (MHz) Re-use or fractional re-use


557

Table 14.9 RAP-dependent simulation parameters. Parameter Max. transmit power (dBm) Antenna gain, omnidirectional (dBi)

Macro BS

Micro BS

Pico BS

RN

46 10

37 2

30 0

37 2

14.5.2.2 Cost-optimal Deployment and Total Deployment Cost of RNs Using the iso-performance approach described in Section 14.4.4.2, an analysis is carried out of the cost-optimal deployment densities for the two RAP types in this example scenario. Figure 14.7 shows the simulated deployment densities (indicated by an ‘×’ symbol), as well as an MMSE fitting curve representing an exponential function of a third-order polynomial through these points. The line of constant cost is defined by its gradient, which is equal to the ratio of total cost per macro BS and RN (see Table 14.11). The cost-optimal deployment is determined by the position at which the line of constant cost meets the iso-performance curve. The optimum numbers or densities of RAPs are given in Table 14.11 and form the basis of the calculation of the total network cost. The total deployment cost is calculated by adding the CAPEX and transformed OPEX cost components for each network element, multiplied by the number of elements for the scenario. The equivalent cost of a deployment with macro BS as the only RAP type would be significantly higher (in excess of 100 million € ), indicating a dramatic efficiency gain by the introduction of RNs (or micro BSs) as supporting nodes. However, this gain can to some extent be attributed to the deployment strategy D2 for macro BSs on a fixed hexagonal grid, which may lead to unfavourable macro-only deployments. The non-uniform BS deployment model D1 addresses this issue (see Section 14.4.3.2). From the calculated cost figures, the cost per area of the example scenario is easily derived (see Table 14.11). For the urban, non-uniform macro BS plus RN deployment, it amounts to 84.20 k€/km2 (without gateway and RRM server). The underlying traffic map exhibits a mean traffic demand of 7260 kbps/km2 . Dividing the cost per area by this figure, the cost per served Mbps is 11.60 k€/Mbps) for an operational period of the network of 10 years. This value can also be used as basis for comparison with systems or deployments serving a different traffic demand. Table 14.10 Number of RNs and micro BSs for deployments based on different macro BS inter-site distances. Macro BS radius 800 900 1000 1100 1200 1300 1400 1500

No. of macro BSs

No. of RNs

No. of micro BSs

RN/micro ratio

224 175 154 120 114 85 80 60

2.9 10.9 15.1 36.9 46.2 120.0 157.4 356.0

2.6 9.6 13.6 33.4 42.5 109.0 142.0 329.0

1.10 1.13 1.11 1.10 1.09 1.10 1.11 1.08

558


Figure 14.6 Indifference map for WA urban, RNs vs micro BSs, densities per km2 .

Figure 14.7 Cost-optimal deployment for WA urban with RNs.


559

Table 14.11 Deployment cost calculation for WA urban, RN deployment.

Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps)

Macro BS

RN

64 397.8 25 459.2

309 26.6 8219.4

Total

33 678.6 84.20 11.60

14.5.2.3 Cost-optimal Deployment and Total Deployment Cost of Micro BSs The focus of the results presented in Case Studies 1 and 2 is on an example cost comparison between the optimum deployments of different types of supporting RAP, for a deployment of macro BSs on hexagonal site positions. We now look at a scenario where micro BSs are deployed instead of RNs. The cost-optimal deployment for micro BSs is presented in the iso-performance curve in Figure 14.8 and in the cost calculation in Table 14.12. The ratio of the total cost of deployment with micro BSs compared to that with RNs is 1.096, indicating a small cost–performance benefit from RN deployment. It should be noted that once-per-network costs, such as gateways and RRM servers, have not been included in this comparison and that the results strongly depend on the assumptions made.

Figure 14.8 Cost-optimal deployment for WA urban, micro BSs.

560


Table 14.12 Deployment cost calculation for WA urban, micro BS deployment. Macro BS Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps) Cost ratio compared to RN supported deployment

RN

70 397.8 27 846.0

Total

215 42.2 9073.0

36 919.0 92.30 12.71 1.096

14.5.2.4 Incumbent vs Greenfield Deployment Costs To facilitate comparison of costs between a deployment with fixed BS positions co-sited with existing 2G or 3G equipment (thus saving the cost of site acquisition) and a deployment with full cost of site acquisition, a price discount can be applied to the existing sites. The deployment can be optimised using an iso-performance curve and slightly different RAP densities result. It has been shown [WIN2D61313] that 5.7 % of the total RAP cost can be saved in the hexagonal scenario if the BS site acquisition cost is dropped. However, a ‘greenfield’ deployment might additionally benefit from more flexible site selection.

14.5.3 Case Studies 3 and 4: WA Rural, Relay Nodes vs Micro BS 14.5.3.1 Simulation Scenario Description The results in this section are based on seven different macro BS radii spanning from 4000 to 7000 m and a network area of 100 ×100 km. Parameter values are found in Section 14.5.1. Further, the values in Table 14.13 are obtained as the mean values of 10 simulations based on randomly generated traffic maps. The cell radii for the RNs and micro BSs are set to 1000 m. This choice guarantees that the RN and micro BS cells are not capacity limited. An indifference map that shows a deployment consisting of macro base stations and either RNs or micro BSs is provided in Figure 14.9. The relay-based deployment requires a higher density of RAPs compared to the micro BS deployment. The reason for this difference is that the radio channel between the macro BS and RN is not as beneficial in a rural scenario (since the other transmissions also have favourable conditions). As stated in Section 14.4.4.1, Table 14.13 Number of RNs and micro BSs for deployments based on different macro BS inter-site distances. Macro BS radius 4000 4500 5000 5500 6000 6500 7000

No. of macro BSs

No. of RNs

No. of micro BSs

RN/micro ratio

224 175 161 120 114 85 80

0.7 9.3 7.7 71.7 101.0 499.5 724.0

0.3 6.0 5.3 39.7 61.7 235.5 325.5

2.00 1.56 1.44 1.81 1.64 2.12 2.22


561

Figure 14.9 Indifference map for WA rural, RNs vs micro BSs.

if the link quality between the macro BS and RN is not so favourable compared to other transmissions, the relay-based deployment is less beneficial. The radio propagation models in Figure 14.2 show that, in the rural scenario, the path-loss difference between a macro BS to RN transmission and a RAP to UT transmission is about 20 dB at 1000 m. The corresponding path-loss difference in an urban scenario is about 40 dB at 1000 m.

14.5.3.2 Cost-optimal Deployment and Total Deployment Cost Again, the deployment of RNs is compared with the deployment of micro BSs. After an optimisation using iso-performance curves, the total cost calculations, are presented in Tables 14.14 and 14.15.

Table 14.14 Deployment cost calculation for WA rural, RN deployment.

Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps)

Macro BS

RN

97 398.81 38 586.6

247 26.60 6570.2

Total

45 156.8 4.52 14.47

562


Table 14.15 Deployment cost calculation for WA rural, micro BS deployment.

Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps) Cost ratio compared to RN supported deployment

Macro BS

RN

91 397.8 3 6199.8

173 42.2 7300.6

Total

43 500.4 4.35 13.94 0.963

As in the urban case, a large number of RNs per macro BS is favourable. Due to a decreased underlying traffic density of 312 kbps/km2 , the total cost per area is much lower than in the urban case, but the cost per Mbps is higher. In this example scenario, the micro BS deployment obtains a slightly better result in terms of the cost for performance. The total cost ratio of the deployment with micro BSs compared to that with RNs in the rural case is 0.963, compared to 1.096 in the urban case.

14.5.4 Case Studies 5 and 6: WA Urban, Relay Nodes vs Micro BS, Intelligent BS Deployment Simulation Scenario Description Since the above case studies did not facilitate a fair comparison between multi-access deployment and a single macro BS deployment, this section introduces ‘intelligent’ macro BS deployment that uses the non-uniform BS deployment strategy described in Section 14.4.3.2. Another motivation is to determine the cost gain of increased site flexibility, e.g., if there are no existing sites that an operator wants to re-use. The iso-performance curve depicted in Figure 14.10 is achieved as follows: 1. A traffic map with size 5 × 5 km is created. 2. An initial deployment of 10 macro BSs is performed. 3. RNs are deployed until the users are satisfied (see Section 14.4.3.2 and the RN-related constraints in Equation 14.9). The result is the leftmost point in the curve, i.e. 10 BSs and 42.1 RNs (a mean of 10 maps). 4. Two additional BSs are added according to the procedure in Section 14.4.3.2. 5. RNs are deployed until the users are satisfied. The result is a combined deployment of 12 BSs and 37 RNs (a mean of 10 maps). 6. Steps 4 and 5 are repeated to generate successives point in the iso-performance curve until the users are satisfied with only BSs. In this scenario it is achieved by 28 BSs (a mean of 10 maps). This procedure is repeated for a number (in this case, 10) of randomly generated traffic maps. For every macro BS added to the initial deployment, the number of required RNs to achieve full coverage is decreased. The deployment continues until the network is covered by only macro BSs (see Table 14.16).


563

Figure 14.10 Indifference map for WA urban, non-uniform position macro BSs and RNs.

Cost-optimal Deployment and Total Deployment Cost It appears that the result is a line with approximately constant slope. This rather surprising result means that the economic trade-off between adding RNs or micro BSs is independent of the existing macro BS density. The slope is around 2.3 for RNs, thus implying that 2.3 RNs can be traded for 1 BS while still achieving equal performance. It also implies that if the cost of 2.3 RNs is lower than the cost of 1 BS, it is economically advantageous to deploy RNs

Table 14.16 Number of RNs and micro BSs for deployments based on different macro BS inter-site distances. No. of macro BSs 28 26 24 22 20 18 16 14 12 10

No. of RNs

No. of micro BSs

RN/mic ratio

0 1.9 4.0 8.4 12.6 17.5 25.4 31.5 37.0 42.1

0 1.6 3.1 6.5 10.5 15.2 17.6 21.8 25.6 28.1

— 1.19 1.29 1.29 1.20 1.15 1.44 1.44 1.45 1.50

564


and this is independent of the existing BS density in the network. The same reasoning can be applied to micro BSs, where the constant slope is 1.55, implying that 1 macro BS can be substituted by 1.55 micro BS and still give equal service. It is also possible to compare the RNs directly with the micro BSs. Here the difference is that the number of RNs is about 50 % higher than the number of micro BSs (the leftmost points contain 28 micro BSs and 42 RNs). This implies that RNs are economically beneficial in this example scenario if micro BSs are 50 % more expensive than RNs. Considering the cost of RNs and micro BSs presented in Tables 14.14 and 14.15, the cost of a micro BS was estimated to be around 50 % more expensive than an RN. Consequently, it is hard to draw conclusions on the most cost-efficient deployment alternative in this scenario.

14.5.5 Case Studies 7 and 8: MIMO Assessment In the WINNER project, the performance of multi-antenna technology in the WINNER RAN has been evaluated using performance figures such as spectral efficiency or total cell throughput [WIN2D341]. While the performance gain due to the deployment of multiple antennas at the base station or terminal is significant, so is the cost increase caused by these enhancements, as has been shown in [WIN2D61313]. In this section, an example MIMO BS deployment is compared to a conventional macro BS deployment model. The cost difference is then compared under the assumption of constant network performance.

14.5.5.1 Deployment Cost Comparison between SISO and MIMO Systems The chosen procedure is to assess the deployment of multi-antenna enhanced RAPs, in this case only macro BSs, and to compare such a deployment with a corresponding SISO deployment. Both SISO and MIMO systems follow the deployment procedure described in Section 14.4.3.2. Differences are accounted for at the radio link level, namely on the SINR. Based on multi-cell, system-level simulations of an interference limited network according to the WINNER simulation assumptions in [WIN2D6137], some performance differences have been established. Four technologies are evaluated: besides the SISO alternative, 2 × 2 MIMO and 4 × 1 MISO are considered. In the case of 2 × 2 MIMO, the base station transmits two separately encoded streams for two different antennas using the scheme referred to as per antenna rate control (PARC). The mobile receiver uses IRC to suppress interference; successive interference cancellation (SIC) after channel decoding is used to remove interference caused by already decoded streams. Such a receiver is here denoted an IRC–SIC receiver. For the 4 × 1 MISO scheme, four antennas at the base station are used for transmit beamforming and a single receive antenna is used at the mobile receiver. Since the more advanced antenna configurations translate to better radio transmission performance, it is clear that more users can be served by a specific BS and therefore a sparser BS deployment can be enabled. The more sophisticated equipment also results in a higher cost, a fact that has been discussed exhaustively earlier in this chapter. The purpose of this section is thus to evaluate the trade-off between a sparser BS deployment and a higher BS hardware cost.


565

Figure 14.11 Post-receiver SINR distributions.

14.5.5.2 Performance Improvement from the Use of Multiple Antennas The improvements stemming from more advanced antenna configurations translate to a higher SINR level compared to the baseline SISO performance. The SINR distributions in Figure 14.11 depict the performance of the MIMO technologies. Note that for 2 × 2 MIMO, two streams are transmitted but the SINR is measured per stream. The difference between the two streams is due to the SIC receiver. When decoding the first stream, the second stream acts as interferer. When decoding the second stream, the signal associated with the first stream is regenerated and subtracted from the input by the SIC receiver and hence the first stream does not cause any interference to the second stream.

14.5.5.3 Deployment Evaluation The deployment strategy is the same as that described in earlier sections, i.e. an initial round of BSs is deployed and additional BSs are added in capacity-limited cells (areas). The simulation assumptions are listed in Table 14.8 and the urban traffic model is used. The simulated network area is 5 × 5 km. As an average of 10 deployment realisations with different traffic maps, 26.9 MIMO-enabled macro BSs are required for SISO, 16 for 2 × 2 MIMO (PARC–SIC) and 11.9 for 4 × 1 MISO. As expected, deploying more advanced antenna equipment enables a sparser BS density.

566


Table 14.17 Total network deployment cost for the antenna configurations.

Hardware cost (k€) Total cost per BS (k€) No. of BSs Total network cost (k€) Performance vs cost gain (relative to SISO)

SISO

PARC–SIC (2 × 2)

MISO–GoB (4 × 1)

30 397.75 26.9 10 699 1.00

44.1 411.85 16 6590 1.62

82.5 450.25 11.9 5358 2.00

14.5.5.4 Cost Assessment of the Multi-antenna Configurations Table 14.17 presents the cost-related values of the antenna configurations. It should be pointed out that the simulations consider only the downlink and that the potential increase in cost for the terminal, e.g. for the SIMO scheme, is not included. However, using this simulation setup and with the assumptions made here, it is clear that the increase in hardware cost due to a more sophisticated antenna configuration is justified by the economic gain stemming from the ability to reduce the number of required BSs and still support the same user traffic demand.

14.6 Conclusion In this chapter, a full cost assessment framework was developed and applied to a range of example WINNER deployments. Although the absolute cost results depend significantly on the estimated figures per cost component and on the deployment strategy and environment, consistent results were obtained that demonstrate the derivation of meaningful statements in a relative comparison of the analysed deployment options. The deployment cost optimisation method based on iso-performance curves was derived under the assumption that the total RAN deployment cost can be approximately expressed as a linear combination of the numbers of different RAP types. In this case, a selection of valid deployment RAP densities, meeting the underlying traffic demand, is made by applying a line of constant cost to the iso-performance curve. The presented deployment models are based on heterogeneous traffic maps, propagation based on WINNER channel models, and RAP capacity calculation using finite traffic elements. The deployment model can be applied to WINNER and other cellular RAN systems employing different RAP types. In an urban area WINNER scenario example, it was observed that a deployment of RNs supporting macro BSs requires a higher density of RAPs than micro BSs supporting macro BSs, due to the in-band BS–RN link. The total cost of the deployment with micro BSs compared to that with RNs, however, was shown to be slightly higher because RNs also offer a substantial cost saving due to the missing leased-line backhaul. The optimum deployments are characterised by a very large number of supporting RAPs (either micro BSs or RNs). In another deployment example considering a rural area case with reduced traffic demand, the cost advantage of RNs compared to micro BSs no longer applied. Furthermore, the total deployment cost per area is much lower compared to the urban case, but the cost of providing a Mbps to the user is higher.


567

For MIMO deployments, a performance versus cost gain could be clearly observed in dedicated optimised-deployment simulations. In the MIMO deployment scenario under consideration, the 4 × 1 MISO–GoB configuration exhibited a gain factor of 2.00 and the 2 × 2 PARC–SIC configuration resulted in a gain factor of 1.62 over the SISO case. It should be noted that all cost gains strongly depend on the assumptions made and that separate cost estimations and optimisations are necessary for each specific deployment scenario to be assessed.

Acknowledgements The work described in this chapter has been carried out within Task 11 (Standardisation, Requirements, and Deployment) of the WINNER II project. The editors would like to thank all colleagues who were active in the research work for the cost assessment activities, and provided material for this chapter, especially:

r Per Skillermark, Ericsson Research, Sweden; r Wsewolod Warzanskyj and Ignacio Berberana, Telefónica I + D, Spain; r Mark Naden, Nortel, UK; r Paulo Jesus and Carlos Silva, Portugal Telecom Inovaça˜ o, Portugal; r Hans Schotten, Qualcomm, Germany (now at the University of Kaiserslautern, Germany). The reviewing efforts of Bernhard Walke, RWTH Aachen University, are acknowledged. They contributed to a further clarification of the methodologies and statements presented in this chapter.

References [FAJ05]

[JF07] [JFK+04] [MSJ+07] [Sta96] [SW07]

[Tim05] [TONIC11] [W+08]

Furuskär A., Almgren M. and Johansson, K. (2005) ‘An infrastructure cost evaluation of singleand multi-access networks with heterogeneous user behavior’, Proc. of IEEE VTC Spring 2005, Stockholm. Johansson, K. and Furuskär, A. (2007) ‘Cost efficient deployment strategies for heterogeneous mobile data systems’, Proc. of IEEE VTC Spring 2007, Dublin. Johansson, K., Furuskär, A., Karlsson, P. and Zander, J. (2004) ‘Relation between base station characteristics and cost structure in cellular system’, Proc. of IEEE PIMRC 2004, Barcelona. Moberg, P., Skillermark, P., Johansson, N. and Furuskär, A. (2007) ‘Performance and cost evaluation of fixed relay nodes in future wide area cellular networks’, Proc. of IEEE PIMRC 2007, Athens. Stanley, R.A. (1996) ‘A methodology for evaluating and optimizing wireless system infrastructure costs’, Proc. of IEEE PIMRC 1996, Athens. Schultz, D.C. and Walke, B.H. (2007) ‘Fixed Relays for Cost Efficient 4G Network Deployments: An Evaluation’, Proc. of IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), Athens, Greece. Timus, B. (2005) ‘Cost analysis issues in a wireless multihop architecture with fixed relays’, Proc. of IEEE VTC Spring 2005, Stockholm. TONIC (2002) IST-2000-25172 Final results on seamless mobile IP service provision economics, Deliverable 11. Werner, M. et al. (2008) ‘Cost Assessment and Optimization Methods for Multi-Node Radio Access Networks’, IEEE VTC Spring 2008, Singapore.

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[WIN2D111]


WINNER II (2006) IST-4-027756 WINNER II Interim Channel Models, Deliverable D1.1.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+. [WIN2D341] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined Spatial-Temporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects. celtic-initiative.org/winner+. [WIN2D351] WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6112] WINNER II (2006) IST-4-027756 Key Scenarios and Implications for WINNER II, Deliverable D6.11.2, September 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6137] WINNER II (2006) IST-4-027756 WINNER II Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D61313] WINNER II (2007) IST-4-027756 Deployment cost assessment for the WINNER II system concept, Deliverable D6.13.13, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [Zan02] Zander, J. (2002) ‘Affordable multiservice wireless networks – research challenges for the next decade’, Proc. of IEEE PIMRC 2002, Lisbon. [Zan97] Zander, J. (1997) ‘On the Cost Structure of Future Wideband Wireless Access’, Proc. of IEEE VTC 1997, Phoenix.

Index 3GPP, 2, 5, 45, 93 3GPP2, 2, 5, 45 AAA server, 105 Active mode, 383–5, 386 Adaptive multi-user transceiver, 243 Admission control, 112, 394–6, Alamouti, 227, 495, 505–6 AMPS, 2 Angle of arrival, 45, 59, 64, 78–80, 87, 233 Angle of departure, 45, 59, 64, 78–80, 87, Antenna bore-sight, 489–90 AoA see Angle of arrival AoD see Angle of departure ARIB, 7 ARQ outer, 311, 434 inner see Hybrid ARQ see also Relay ARQ Array gain, 220 Average session duration, 466, 474–5 Azimuth spread, 58, 61 Base coverage urban (test scenario), 486–93, 495–513 Base station, 96–7, 106–7 parameter, 487, 489 Radio Technologies and Concepts for IMT-Advanced C 2009 John Wiley & Sons, Ltd

Beamforming, 232–43 Beams fixed, 233, 366 grid of, 233, 366, 495–500, 509–513 Bit-interleaved coded modulation (BICM), 136, 484 Block Low-Density Parity Check (BLDPC) encoding, 139 codes (BLDPCC), 138 decoding, 141 Broadcast, 102, 111, 117 see also Multicast Candidate spectrum bands, 440, 442, 478 Capacity requirement calculation, 465, 469–70, 474–6 CAPEX, 531–5 CBR see Constant bit rate model CDL model see Clustered delay line model cdma2000, 2 Cell edge performance, 283, 493, 499, 501, 504–6 user throughput, 241, 243, 270, 493, 502 Cell range, 490 CEPT, 2 Channel allocation dynamic channel allocation (DCA), 357 fixed channel allocation (FCA), 357

Martin Döttling, Werner Mohr and Afif Osseiran

570

Channel estimation, 179 cellular interference, 182 degradation, 184, 186, 190 genetic algorithm (GA) aided, 185 iterative (ICE), 180, 182 MIMO, 185 pilot-aided (PACE), 180, 181 prediction, 190 Channel impulse response, 72, 84, 87 Channel measurement, 47, 55–9 Channel prediction see Channel estimation, prediction Channel quality indicator (CQI), 169, 228, 257, 259, 332–5 delay, 252–3 Channel state information (CSI), 170, 228, 259 effective (ECSI), 170 feedback, 200 long-term, 170, 366 quantisation errors, 200 short-term, 170 transmitter (CSIT), 170 Channel symbol, 118, 333 Chase combining, 163–5, 496, 501, 505, 509 Chunk, 118, 224–5 layer, 119, 224–5, 237 Clustered Array, 243 Clustered delay line model, 86–7 Code Division Multiple Access (CDMA), 2, 328 Multi-carrier CDMA (MC-CDMA), 329 Common phase error (CPE), 196–198 Competition band, 177, 269 Concatenation, 114, 119–20, 122 Congestion control, 404–5 Constant bit rate model, 16, 508 Control plane, 111–12, 307 Control signalling, 95, 125–6, 228 Cooperative diversity see Diversity, cooperative Cooperative relaying, 258, 260, 304–6 Coordinated Multipoint (CoMP) Transmission see Distributed antenna systems

Index

Correlation, 44–7, 86 of large-scale parameters, 64–68 distance, 68, 79, 546 parameters, 78–80 Cost assessment framework, 526 components, 530 figures, 537 of relays, 293 optimisation, 555 Cross-correlation of parameters, 64–8, 78, 80 Cross-polarisation (XPR), 79 Cyclic delay diversity (CDD), 369–72 DARPA, 7 DAS see Distributed antenna systems Data rate peak, 2, 31 sustainable, 31 Dedicated band, 434, 446 Delay spread, 45, 61, 64, 78, 81, 85–6, 327 Delay, propagation, 209 HARQ, 164 multi-hop, 289, 306 requirement of service classes, 21–2 see also Packet delay transmission, 348 user, achievable, 32 definition, 31 Deployment scenario, 18, 19 local area, 19, 517 metropolitan area, 19 wide area, 18 Distributed antenna systems (DAS), 258, 261 Distributed MIMO see Distributed antenna systems Diversity cooperative, 260–2, 271 distributed, 261 gain, 221 macro, 105, 304, 368, 505 receive, 231, 370 transmit, 370

Index

Doppler, 44, 62–3, 87 181 Double directional, 40, 47 Drop, 68–9 DRS see Dynamic Resource Sharing Duo-Binary Turbo Codes (DBTC), 151–60 Dynamic Resource Sharing (DRS), 301, 503–4 E2E ARQ see ARQ, outer EDGE, 2 Elevation spread, 80–1 Equipment sharing, 101, 440 Erlang-B formula, 469, 476 ETRI, 7 European Commission, 1, 3 Exclusion zone, 422 424, 442–4 Exponential distribution, 65, 67–8, 72 Extension band, 435 Fast fading, 45, 69 351, 482 FDD, 117–19, 341–2 Feedback, 125, 182, 255–8, 335 Firefly synchronisation, 205 compensating propagation delays, 210 pulse-coupled oscillators, 206 rules, 207 Fixed satellite services (FSS), 420 Flashlight effect, 251 Flexible spectrum use (FSU), 419 Flow, 105 class, 105–6, 119–121, 347 control, 106, 401–4 Forward error correction (FEC) code, 137, 222–4, 433 blocks, 120, 129 Frame, 117–9, 330, 335, 492–3 Frequency Division Multiple Access (FDMA), 326 Block Equidistant FDMA (B-EFDMA), 331 Block Interleaved FDMA (B-IFDMA), 331, 337 Interleaved FDMA (IFDMA), 337 Localised FDMA (LFDMA), 337

571

Frequency re-use, 188, 360 fractional (FFR), 361, 364, 516–8 soft (SFR), 302; 360, 514–8 FuTURE Forum, 6 FuTURE project, 6 Fuzzy Logic, 407 Gateway, 96, 98–9, 397, 398, 401 logical node, 96 control gateway, 96, 104–6 IP anchor gateway, 96, 104–6 Generalised multi-carrier modulation (GMC), 95, 337 Generic (channel) model, 47, 63, 70, 78, 80, 86 Genetic Algorithm see Channel estimation, genetic algorithm aided Geometry-based (stochastic) channel model, 40, 46, 47, 59 Global time reference, 210 GoB see Beams, grid of GPRS, 2 Grid-based scenario, 41, 42, 80 GSM, 2 Handover, 99–100, 106 horizontal, 4 intermode, 390–2, 409, 410–4 intramode, 389–90, 410–2 intersystem, 392–3, 407 IP handover, 381, 388–9, 393 vertical, 4 Hardware sharing see Equipment sharing High power amplifier (HPA), 192 backoff, 193, 194 Higher order sectorisation (HOS), 333 Horizontal sharing, 422 HSPA, 2 Hybrid automatic repeat request (H-ARQ), 113–15, 126–7, 162–4 context transfer, 434 segment 120–2 process, 120

572

Hybrid Information System (HIS), 385 Idle mode, 383–5 IEEE standardisation, 6 IEEE802.xx series, 3, 7, 279 Impulse response, 44, 48, 59, 72, 85 see also Channel impulse response IMT-2000, 2, 5, 460–1, 464, 478 IMT-Advanced, 1, 3, 460–4, 477–8 Incremental redundancy, 120 cyclic, 163–5 Indifference curve, 294, 295, 550–1 Indoor scenario, 486–93, 517–521 coverage, 501–3, 506–8, 513–4 Inter-GW handover, 393 Inter-base-station communication (IBSC), 435–40 Inter-carrier interference (ICI), 196 Interference cancellation (IC), 352–7 direct IC, 354 Indirect IC, 354 inter-cell IC (IIC), 353 Parallel IC (PIC), 353 Interference coordination, 360, 514 Interference mitigation, 250, 326, 349 Interference rejection combining (IRC), 232, 370 International Telecommunication Union, 1, 3, 4, 459–61 Interpolation, 179–80, 181 Inter-site distance, 487, 489–90 IP convergence layer (IPCL), 109–110, 112–14, 378, 381 IPCL see IP convergence layer IS–95 cdma, 2 ISD see Inter-site distance Iso-performance, 550 Iterated block decision feedback equaliser (IBDFE), 184, 187 Iterative channel estimation (ICE) see Channel estimation, Iterative (ICE)

Index

ITU see International Telecommunication Union Kalman Predictor, 191 Laplacian distribution, 45, 87 Large-scale parameter, 48, 64–70 LDC see Linear dispersion code Least satisfaction algorithm, 453 Line of Sight (LoS), 41–3, 54, 71, 75–9 model parameters, 78–81 probability, 80 Linear dispersion code (LDC), 221, 270 adaptive, 237 Link level procedures, 169 Link-to-system interface, 351, 483–4 Load balancing, 100, 106 between gateways, 394 between RAPs, Load control see Congestion control Logical channel, 109–10, 114–16, 123–4, 396 LBCCH, 123 LCCCH, 124 LDCCH, 124 LDTCH, 124 LMCCH, 124 LMTCH, 124 LPCCH, 124, 385 Logical node, 93, 96, 104–110 architecture, 93, 96–109 Low Density Parity Check Code (LDPCC) see Block Low Density Parity Check code LSP see Large-scale parameters LTE, 2 M/G/1 nonpreemptive priority queue, 470, 476 MAC see Medium access control Manhattan grid, 486–7, 490 Market study, 462, 466, 468

Index

Maximum flexibility algorithm, 453 Maximum number of supported users, 493–4 Maximum ratio combining (MRC), 232, 357, 370 MCBC, see Multicast Mean service bit rate, 466, 468–9 Measurements, 200, 257 metrics, 200 signalling, 200 Medium access control (MAC), 115–117, 119–21, 223, 308relay, 308 transmission control, 346–8 MI-ACM (mutual interference based adaptive coding and modulation), 161, 495–500 Microcellular (test scenario), 486–93, 513–517 MIESM, 484 MIMO see Multiple Input Multiple Output Minimum Mean Square Error (MMSE), 191, 232 linear, 197 successive, see Sucessive MMSE MISO see Multiple Input Single Output mITF, 7 Mobility ratio, 17, 466, 468 Mobility, 16, 25, 98–100, 106–110 MRC see Maximum ratio combining Multi-band scheduler, 122–3, 431–3 Multi-band transmission, 102–4 Multicast, 465, 469, 470, 504–8 see also Multimedia broadcast multicast service Multi-hop network, 281 Multi-user MIMO, concept, 244,247, 248 uplink performance, 269 Multimedia broadcast Multicast Service (MBMS), 102–3, 105, 124–5 Multiple access, 325

573

Multiple input multiple output (MIMO), 219, 220 distributed, 31 model, 44–9, 59, 63 see also Multi-user MIMO Multiple Input Single Output (MISO), 220 MultiSphere level concept, 4 Multi-state channel, 483 Mutual information based adaptive coding and modulation see MI-ACM Mutual information effective SINR mapping see MIESM Network layout, 489–92 NGMC, 7 NGMN, 3 Non Line of Sight (NLoS), 41–2, 56, 57, 72, 77 model parameters, 76, 78–81 NSF, 7 Operational expenditures (OPEX), 294, 436, 531 Opportunity Driven Multiple Access (ODMA), 281 Optimum combining, 232 Orthogonal Frequency Division Multiple Access (OFDMA), 328 Orthogonal pilot set see Pilot, Set Over-the-air (OTA), 435, 438 Packet delay, 31–2, 318, 466, 494, 509–12 Paging, 105, 385, 386 channel, 124,125 indication (PI), 125 message (PM), 125 PARC see Per Antenna Rate Control PAS see Power azimuth spectrum Path loss, 45, 71, 75–7, 278–9 PDC, 2 PDU see Protocol data unit

574

Peak-to-average power ratio (PAPR), 193, 202 Per stream rate control (PSRC), 226 Phase locked loop (PLL), 198 Phase noise, 195 linear minimum mean squared error (LMMSE), 197 model, 196 OFDM, 196 serial modulation, 198 Phase response curve (PRC), 206 Physical channel, 126, 131 PADC, 129 PBCH, 127 PDCFC, 128 PMBC, 129 PNDC, 128–30 PRACH, 117-0, 130 PUCH, 130–1 Physical layer, 125–31 Physical node, 96 Pilot design, 169, 253–6 capacity, 179 downlink, 174 FDD, 174, 176 preamble, 177 TDD, 175, 177 uplink, 175, 179 Pilot preamble, 117–8 synchronisation, 130–1 boost, 174 common, 171 dedicated, 172 grid, 169, 171, 172, 174 overhead, 175, 184 pattern 170, 172 re-use, 173, 182 sequence, 174 set, 171 spatial multiplexing see Pilot, re-use type, 171 Pilot-aided channel estimation (PACE) see Channel estimation, Pilot aided

Index

Point-to-multipoint see Multicast Pool area see Pool concept, Pool concept, 98–102 Power azimuth spectrum, 87 Power spectrum sidelobe, 193 Propagation scenario, 41, 71 Protection distance, 442, 444–6 Protocol architecture, 94, 109 data unit (PDU), 110, 114, 119, 378 Pulse-coupled oscillator, 205 see also Firefly Synchronisation QC-BLDPC see BLDPC Radio access technique group, 462, 467–8 Radio environment, 467, 468, 475 Radio handover, 387–9 see also Handover Radio link control (RLC), 114–15, 311, 382, 432 Radio regulations, 459–61 Radio resource control (RRC), 110–12, 381, 383–5 Radio resource management, 406centralised, 406 common, 409 distributed, 406 hybrid, 407 in relay-enhanced cells, 297 Random vector quantisation (RVQ), 201 Rapp model, 193 RAT group see Radio access technique group Reference BS, 210 Reference design, 93 Refractory period, 207 Regularised block diagonalisation (RBD), 249–51, 266–9 Relay Alamouti diversity, 261 Relay coherent combining (RCC), 261 Relay cyclic delay diversity (RCDD), 261 Relay node, 96–7, 107, 277, 399

Index

Relay ARQ, 311 deployment, 282–92 deployment cost, 293 parameter, 488–9 relay-enhanced cell (REC), 279 Relaying amplify-and-forward, 261, 281 cooperative Relaying (CR), 304 decode-and-forward, 261, 296 MIMO, 392 performance assessment, 312–19, 501–8, 513–21 Requirement coverage, 30 delay, 31–2 measurements, 26 performance, 29 spectrum fragmentation, 34 range, 34 system, 24 Resource allocation, 121–2, 299, 308–10, 336–8, 503 Resource negotiation, 425 Resource partitioning, 117, 310, 490, 519 load-based, 299 dynamic, 503 fixed, 501 with soft frequency, 516 Retransmission delay, 348–9 unit, 114, 119 Ricean K-factor, 64, 71 RLC see Radio link control RRC see Radio resource control RRM server, 96–8, 108, 378, 395, 396, 399 Satisfied user criterion, 246, 493–4 Scheduler, 115–7, 120–3, 496–8, 501 delay-aware, 509–3 Maximum C/I, 496 phased approach, 515, 517

575

proportional fair, 496, 498, 509–13 round robin, 482, 496–8, 501, 503 SDMA see Space division multiple access SDU see Service data unit Segmentation, 114–15, 119–20 Service access points, 110, 223 Service category, 466 Service data unit (SDU), 110, 378 Service environment, 467 Session arrival rate per user, 466, 475 SFN see Single frequency network Shadow fading, 58, 60, 64–8, 75–76, 78–9 SIMO see Single Input Multiple Output Simulation parameters deployment-specific, 487–91 environment-specific, 486 Simulation dynamic system level, 482 link level, 482 protocol level, 482 quasi-static system level, 48 static system level, 482 Simulator see Simulation Single frequency network (SFN), 103, 505–8 Single Input Multiple Output, 219 SISO see Single Input Single Output Site-sharing, 436, 529 Slot, 117–19 Small-scale parameter, 59, 72 Snapshot, 45 Soft frequency re-use see Frequency re-use Space time Coding 221 Space-time block coding, 495, 500 S-PARC, 497, 500 Spatial adaptation, 229 Spatial division multiple access, 228, 233, 496–500, 511–3 Spatial multiplexing gain, 221

576

Spatial user selection, 230 Spectral efficiency, 32, 493–4, 496, 498–500, 504, 513 Spectrum assignment, 420, 423 long term, 423, 425–7, 447–51 short term, 424, 427–9, 430, 451–4 Spectrum beacon channel, 446 Spectrum calculation methodology, 461–72, tool, 461, 472–7 Spectrum demand, 459–60, 477–8 see also Spectrum calculation Spectrum identification for IMT, 459–60, 463, 478 Spectrum manager, 430 Spectrum mask, 193 Spectrum register, 424 Spectrum resource change (SRC), 426 Spectrum server, 96–8, 108–9 Spectrum sharing, 420 and coexistence (SSC), 35, 419 Spread Spectrum Multi-Carrier Multiple Access (SS-MC-MA), 329 STBC see Space time block coding STC see Space time coding SUC see Satisfied user criterion Successive MMSE, 248–51, 268–70 Super-frame, 117–8, 297–8, 308–10 Synchronisation frequency, 201, 203 link, 201 narrowband interference (NBI), 204 network see Firefly synchronisation of BS, 506–8 preamble, 201, 204 time, 201, 202 System concept, 93 System information, 113, 387, 400 System packet delay, 494

Index

TCP, 378–80, 410 TDD, 117–9 Teledensity, 3, 467, 476 Test scenario, 485 Time Division Multiple Access (TDMA), 327 Timing advance procedure, 209 T-Pilot, 201 Traffic calculation, 468 Traffic distribution, 469, 472, 542 Traffic map heterogeneous, 542 Traffic FTP, 23 interactive applications, 24 internet and multimedia, 22 model, 20 streaming, 23 video telephony, 23 Voice over Internet Protocol (VoIP), 23 Transmission mode frequency-adaptive, 331 non-frequency-adaptive, 331 Transmitter generic, 221, 222 Transport block, 114, 119, 223–5, 347 Transport channel, 110, 123–5, 131 TBCH, 124 TMCH, 125 TPCH, 125 TRAC, 125 TSCH, 125 Turbo equalisation, 190 Two-dimensional cyclic prefix (2D-CP), 262 UMTS, 2, 5 Unicast, 102–3 traffic, 465, 469 User density, 16, 466, 474–5, 542 User grouping, 229 User mobility see Mobility User packet delay see Packet delay

Index

User plane, 109–10, 381, 384 User terminal, 108, 498–9 User throughput, 30, 493–4 UT Active see Active mode UT Detached, 383 UT Idle see Idle mode Van diagram, 5 Vertical sharing, 420–1

577

Wall loss, 41 Wiener filter, 181 model mismatch, 181 Wiener process, 196 WINNER Radio Access Network (WRAN), 93, 96 WINNER, 1, 3, 7, 8 World Radiocommunication Conference (WRC), 1, 3, 419 WWI, 9 WWRF, 3, 4

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