Optical Performance Monitoring: Advanced Techniques for Next-Generation Photonic Networks

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Optical Performance Monitoring

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Optical Performance Monitoring Advanced Techniques for Next-Generation Photonic Networks

Calvin C. K. Chan, Ph.D. The Chinese University of Hong Kong

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, California 92101-4495, USA The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1 GB, UK # 2010 ELSEVIER Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data Chan, Calvin C. K. Optical performance monitoring : advanced techniques for next-generation photonic networks / Calvin C. K. Chan. p. cm. Includes bibliographical references and index. ISBN 978-0-12-374950-5 (alk. paper) 1. Optical fiber communication. 2. Network performance (Telecommunication) I. Title. TK5103.592.F52C48 2010 621.3820 75—dc22 2009046134 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. For information on all Academic Press publications visit our Web site at www.elsevierdirect.com Printed in The United States of America. 10 11 12 9 8 7 6 5 4 3 2

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To my beloved wife Esther and our lovely kids, Tsz-ching and Lang-ho.


Contents List of Acronyms................................................................................................................................. xv List of Figures .................................................................................................................................... xxi List of Tables....................................................................................................................................... xli Preface ............................................................................................................................................... xliii Acknowledgments .............................................................................................................................. xlv List of Contributors .......................................................................................................................... xlvii About the Editor ................................................................................................................................ xlix

CHAPTER 1 OPTICAL PERFORMANCE MONITORING: PERSPECTIVES AND CHALLENGES ......................................................................................... 1 1.1 Introduction................................................................................................................. 1 1.1.1 Overarching vision .............................................................................................2 1.1.2 Challenges ..........................................................................................................3 1.2 Physical-Layer Measurements and Routing Decisions in Today’s Optical Networks ........................................................................................................ 4 1.3 Signal Parameters Requiring Monitoring and OPM Techniques ............................. 5 1.3.1 Optical impairments ...........................................................................................5 1.3.2 OPM techniques .................................................................................................8 1.4 Laudable OPM-Enabled Functionalities in Next-Generation Optical Networks ..... 9 1.4.1 Robust and stable operation...............................................................................9 1.4.2 Accommodate transparency .............................................................................10 1.4.3 Impairment-aware routing................................................................................10 1.4.4 Secure links ......................................................................................................12 1.4.5 Optical supervisory channel.............................................................................13 1.5 Smart Network Operation and Security................................................................... 13 1.5.1 Smart network operation..................................................................................14 1.5.2 Security.............................................................................................................14 1.6 Summary ................................................................................................................... 16

CHAPTER 2 OPTICAL SIGNAL-TO-NOISE RATIO MONITORING ............................... 21

2.1 Introduction............................................................................................................... 21 2.2 Linear Interpolation Techniques .............................................................................. 22 2.2.1 Optical spectrum analysis ................................................................................22 2.2.2 Out-of-band noise measurement ......................................................................23 2.2.3 Potential problems............................................................................................24 2.3 Polarization-Based Techniques ................................................................................ 26 2.3.1 Operating principles .........................................................................................26 2.3.2 Potential problems and limitations ..................................................................30 2.3.3 Methods to overcome limitations ....................................................................36

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2.4 Interferometer-Based Technique .............................................................................. 41 2.4.1 Operating principle...........................................................................................41 2.4.2 Potential problems and limitations ..................................................................42 2.4.3 Method to overcome limitations......................................................................42 2.5 Beat Noise Analysis Techniques ............................................................................. 43 2.5.1 Operating principle...........................................................................................43 2.5.2 Potential problems and limitations ..................................................................45 2.5.3 Methods to overcome limitations ....................................................................47 2.6 OSNR Estimation Technique Based on the Operating Condition of Optical Amplifiers .............................................................................. 56 2.6.1 Operating principle...........................................................................................56 2.6.2 Link-based OSNR monitoring technique ........................................................57 2.6.3 Potential problems and limitations ..................................................................59 2.7 Summary ................................................................................................................... 61

CHAPTER 3 CHROMATIC DISPERSION MONITORING............................................. 67 3.1 Introduction............................................................................................................... 67 3.2 Chromatic Dispersion and Its Effects on Optical Fiber Systems ........................... 68 3.2.1 Fiber chromatic dispersion...............................................................................68 3.2.2 Systems limitations due to chromatic dispersion............................................70 3.2.3 Dispersion effects in the presence of fiber nonlinearities ..............................73 3.2.4 The need for chromatic dispersion monitoring...............................................75 3.3 Chromatic Dispersion Monitoring Techniques........................................................ 77 3.3.1 Measurement of RF spectrum .........................................................................77 3.3.2 Measurement of relative group delay between VSB signals .........................80 3.3.3 Histogram monitoring techniques....................................................................81 3.3.4 All-optical spectral analysis using nonlinear optics .......................................85 3.3.5 Electronic monitoring techniques ....................................................................88 3.3.6 Other chromatic dispersion monitoring techniques ........................................89 3.3.7 Differentiate chromatic dispersion from polarization mode dispersion.........90 3.4 Summary ................................................................................................................... 92 Acknowledgments ........................................................................................................... 92

CHAPTER 4 POLARIZATION MODE DISPERSION MONITORING ............................ 101

4.1 Introduction............................................................................................................. 101 4.2 PMD Monitoring Based on Measurement of RF Tone ........................................ 106 4.3 PMD Monitoring Based on Measurement of Degree of Polarization .................. 111 4.4 Electronic PMD Monitoring Techniques............................................................... 117 4.5 Other PMD Monitoring Techniques ...................................................................... 121 4.6 Summary ................................................................................................................. 121 Acknowledgments ......................................................................................................... 122

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CHAPTER 5 TIMING MISALIGNMENT MONITORING............................................. 127 5.1 Introduction............................................................................................................. 127 5.2 Monitoring of Timing Alignment .......................................................................... 128 5.2.1 Synchronization of pulse carver and data modulation .................................128 5.2.2 Synchronization for phase remodulation.......................................................134 5.2.3 Synchronization for I/Q data and data/pulse carver .....................................134 5.2.4 OTDM clock recovery using timing misalignment of data pulses ..............136 5.3 Investigation of the Effects of Timing Misalignment........................................... 137 5.3.1 Clock/data synchronization in CPFSK systems ............................................137 5.3.2 Misalignment between pulse carver/data modulator in RZ-DPSK systems............................................................................................................137 5.3.3 Misalignment between ASK and DQPSK modulation in ASK/DQPSK orthogonal modulation systems..............................................138 5.4 Mitigation of Timing Misalignment ...................................................................... 140 5.4.1 Hybrid OTDM scheme for demultiplexing with better timing misalignment tolerance ..................................................................................140 5.4.2 Novel remodulation scheme for colorless high-speed WDM-PON without remodulation synchronization .....................................140 5.4.3 Misalignment mitigation using MLSE equalizers ........................................141 5.5 Summary ................................................................................................................. 141

CHAPTER 6 OPTICAL PERFORMANCE MONITORING BASED ON ASYNCHRONOUS AMPLITUDE HISTOGRAMS ................................... 145 6.1 Introduction............................................................................................................. 145 6.2 Monitoring Techniques Based on Analysis of Asynchronous Histograms .......... 149 6.2.1 Q-factor monitoring .......................................................................................149 6.2.2 OSNR monitoring using asynchronous histograms ......................................157 6.3 General Concepts on the Acquisition and Processing of Amplitude Histograms .............................................................................................................. 163 6.3.1 Sampling noise ...............................................................................................163 6.3.2 Averaging effects ...........................................................................................168 6.4 Summary ................................................................................................................. 172

CHAPTER 7 OPTICAL PERFORMANCE MONITORING BASED ON ASYNCHRONOUS DELAY-TAP SAMPLING......................................... 175 7.1 Introduction............................................................................................................. 175 7.2 Technique................................................................................................................ 176 7.2.1 Phase portrait..................................................................................................177 7.2.2 Pattern recognition .........................................................................................180 7.3 Experiment .............................................................................................................. 182 7.3.1 Network emulator...........................................................................................182

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7.3.2 Multi-impairment monitor .............................................................................183 7.3.3 First-order PMD .............................................................................................183 7.3.4 Results for 10-G NRZ and 40-G NRZ-DPSK ..............................................183 7.3.5 System testing ................................................................................................185 7.4 Discussion ............................................................................................................... 187 7.4.1 Extension to new impairments ......................................................................188 7.4.2 Application to higher-order formats..............................................................188 7.5 Summary ................................................................................................................. 191

CHAPTER 8 OPTICAL PERFORMANCE MONITORING BASED ON OPTICAL SAMPLING .......................................................................... 193

8.1 Introduction............................................................................................................. 193 8.1.1 Data encoding in the electric field of optical waves....................................193 8.1.2 Temporal characterization of optical signals ................................................194 8.1.3 Linear optical sampling .................................................................................196 8.2 LOS Principle and Properties................................................................................. 196 8.2.1 Coherent detection .........................................................................................196 8.2.2 Various implementations of coherent detection for optical performance monitoring ..........................................................................................199 8.2.3 Polarization and wavelength sensitivity ........................................................201 8.2.4 Phase sensitivity .............................................................................................202 8.2.5 Digital phase tracking ....................................................................................204 8.3 Implementations of LOS ........................................................................................ 205 8.3.1 Balanced photodetection ................................................................................206 8.3.2 Direct photodetection .....................................................................................208 8.3.3 LOS with four-wave mixing..........................................................................208 8.3.4 Correction of imperfections ...........................................................................209 8.4 Optical Performance Monitoring with LOS .......................................................... 211 8.4.1 Characterization of amplified spontaneous emission....................................211 8.4.2 Phase and amplitude noise measurements ....................................................211 8.4.3 Nonlinear phase noise ....................................................................................213 8.4.4 Nonlinear phase shift measurement...............................................................216 8.4.5 Digital processing of sampled electric field .................................................216 8.4.6 Characterization of the electric field of periodic sources ............................217 8.5 Recent Results and Related Techniques................................................................ 218 8.6 Summary ................................................................................................................. 219 Acknowledgments ......................................................................................................... 219

CHAPTER 9 OPTICAL PERFORMANCE MONITORING BASED ON RF PILOT TONES.......................................................................... 223

9.1 Introduction............................................................................................................. 223

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9.2 Performance Monitoring Techniques Using AM Pilot Tones .............................. 224 9.2.1 Operating principle.........................................................................................224 9.2.2 Potential problems..........................................................................................225 9.2.3 Scalability .......................................................................................................232 9.2.4 Typical applications .......................................................................................234 9.3 Performance Monitoring Techniques Using PM and FM Pilot Tones................. 238 9.3.1 Using PM pilot tones .....................................................................................238 9.3.2 Using FM pilot tones .....................................................................................243 9.4 Dispersion Monitoring Techniques for Adaptive Compensators.......................... 245 9.4.1 CD monitoring techniques using AM and PM pilot tones...........................246 9.4.2 CD monitoring technique using chirped pilot tone ......................................249 9.4.3 CD monitoring technique using pilot tone carried by broadband light source...................................................................................252 9.4.4 PMD monitoring technique using SSB pilot tone ........................................256 9.5 Summary ................................................................................................................. 258

CHAPTER 10 OPTICAL PERFORMANCE MONITORING BASED ON ELECTRONIC DIGITAL SIGNAL PROCESSING .................................. 261 10.1 Introduction........................................................................................................... 261 10.2 OPM in Digital Direct Detection Systems .......................................................... 263 10.2.1 The channel model for direct detection systems ......................................264 10.2.2 State-based equalization based on MLSE .................................................265 10.2.3 State-based OSNR estimation....................................................................268 10.2.4 Referenced parameter estimation ..............................................................271 10.2.5 Conclusion ..................................................................................................274 10.3 OPM in Digital Coherent Receivers.................................................................... 274 10.3.1 Theory.........................................................................................................275 10.3.2 Joint estimation of linear channel parameters ..........................................280 10.3.3 Conclusion ..................................................................................................295 10.4 Summary............................................................................................................... 296

CHAPTER 11 OPTICAL PERFORMANCE MONITORING BASED ON NONLINEAR OPTICAL TECHNIQUES .................................................................... 301 11.1 11.2 11.3 11.4

Introduction........................................................................................................... 301 Nonlinear Optics................................................................................................... 303 OPM Techniques Using Nonlinear Optics .......................................................... 304 Key Challenges..................................................................................................... 309 11.4.1 Sensitivity ...................................................................................................309 11.4.2 Cost, size, and complexity.........................................................................312 11.4.3 Impairment isolation ..................................................................................312 11.5 Summary............................................................................................................... 314

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CHAPTER 12 OPTICAL PERFORMANCE MONITORING OF OPTICAL PHASE–MODULATED SIGNALS ....................................................... 319

12.1 Introduction........................................................................................................... 319 12.2 Performance of Phase-Modulated Signals........................................................... 320 12.2.1 Signal impairments.....................................................................................321 12.2.2 Generation and detection of N-PSK signals .............................................325 12.3 Optical Performance Monitoring ......................................................................... 327 12.3.1 Monitoring techniques ...............................................................................327 12.3.2 Comparison of monitoring techniques ......................................................343 12.4 Summary............................................................................................................... 345 Acknowledgments ......................................................................................................... 346

CHAPTER 13 OPTICAL PERFORMANCE MONITORING FOR COHERENT OPTICAL SYSTEMS ........................................................................................ 351

13.1 Historical Aspect of Coherent Optical Systems.................................................. 351 13.2 Single-Carrier and Multicarrier Coherent Optical Systems................................ 352 13.2.1 Principle of coherent detection..................................................................353 13.2.2 Single-carrier coherent optical system ......................................................354 13.2.3 Coherent optical OFDM system ................................................................356 13.2.4 Comparison of single-carrier and multicarrier coherent optical system.............................................................................................360 13.3 OPM Using Coherent Detection .......................................................................... 361 13.3.1 OPM without receiver electrical equalization...........................................361 13.3.2 OPM with receiver electrical equalization................................................366 13.4 OPM in CO-OFDM Systems ............................................................................... 368 13.4.1 Optical channel model ...............................................................................369 13.4.2 Principle of OPM through optical channel estimation .............................371 13.5 Progress in OPM for CO-OFDM Systems .......................................................... 373 13.5.1 Simulation model and results ....................................................................373 13.5.2 Optical performance monitoring in CO-OFDM systems with 4-QAM ...375 13.5.3 OPM in CO-OFDM systems with 16-QAM modulation .........................378 13.6 OPM Experiment Results..................................................................................... 379 13.7 Summary............................................................................................................... 381

CHAPTER 14 OPTICAL PERFORMANCE MONITORING IN OPTICAL TRANSPORT NETWORKS ................................................................ 385

14.1 Introduction........................................................................................................... 385 14.2 Overview............................................................................................................... 385 14.2.1 Business interface model ...........................................................................385 14.2.2 Generic OTN service requirements ...........................................................386 14.2.3 OTN: A network of networks....................................................................387

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14.3 Generic Modeling Principles for Transport Networks........................................ 388 14.3.1 Top-level functional architecture...............................................................388 14.3.2 Control plane functions..............................................................................388 14.3.3 Management functions ...............................................................................388 14.3.4 Transport functions ....................................................................................390 14.4 Modeling of Multilayer Networks ....................................................................... 390 14.4.1 Application of partitioning concept...........................................................392 14.4.2 Application of the layering concept ..........................................................392 14.4.3 Transport entities: trails and connections..................................................392 14.4.4 Characteristic information..........................................................................393 14.5 Optical Transport Network–Layered Structure ................................................... 393 14.5.1 OTN-layer networks...................................................................................393 14.5.2 Layer management .....................................................................................394 14.5.3 OTN information structure ........................................................................394 14.6 OTN Services ....................................................................................................... 395 14.6.1 All-Optical Networks .................................................................................395 14.7 Test and Measurement Tasks in Optical Networking......................................... 396 14.7.1 Lightpath provisioning ...............................................................................396 14.7.2 Service assurance .......................................................................................398 14.8 Optical Performance Monitoring ......................................................................... 400 14.8.1 Optical-layer signal quality supervision requirements..............................401 14.8.2 Optical power .............................................................................................403 14.8.3 Channel wavelength ...................................................................................403 14.8.4 OSNR..........................................................................................................403 14.8.5 Q-factor measurement................................................................................407 14.8.6 OTUk, ODUkT, and ODUkP signal quality supervision .........................409 14.8.7 What is missing? ........................................................................................410 14.9 Implementation Issues .......................................................................................... 411 14.9.1 Accuracy requirements...............................................................................411 14.9.2 External versus embedded monitoring ......................................................412 14.9.3 Monitoring points.......................................................................................413 14.9.4 Recommended measurement interval........................................................414 14.9.5 Risk management aspects ..........................................................................415 14.9.6 Improved fault diagnostics by event correlation ......................................417 14.10 Future Challenges............................................................................................... 419 14.11 Summary............................................................................................................. 419

CHAPTER 15 OPTICAL PERFORMANCE MONITORING IN OPTICAL LONG-HAUL TRANSMISSION SYSTEMS ............................................................. 423 15.1 Introduction........................................................................................................... 423

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15.2 Elements of a Long-Haul Transmission System................................................. 424 15.3 System Performance Measures ............................................................................ 430 15.4 OPM in a Long-Haul Transmission System ....................................................... 432 15.4.1 OPM functions and applications ...............................................................432 15.4.2 Optical device monitoring .........................................................................433 15.4.3 OSNR monitoring along transmission line ...............................................435 15.4.4 Transmission performance testing and analysis........................................438 15.4.5 Service-oriented system design .................................................................442 15.5 Summary............................................................................................................... 445 Index................................................................................................................................... 447

List of Acronyms 2R 3R AAH ACRZ ADC AF AIS AM AMI ANN AON APS ASE ASIC ASK ASTN AWG AWGN BER BIP BLS BPF BPSK CCI CD CI CM CMA CMOS CNR CO-OFDM CP CPE CPFSK CSRZ CW DB DBPSK DBR DC

Regeneration, reshaping Regeneration, reshaping, retiming Asynchronous amplitude histogram Alternate chirped return to zero Analog-to-digital converter Amplitude filter Alarm indication signal Amplitude modulation Alternate mark inversion Artificial neural network All-optical network Automatic protection switching Amplified spontaneous emission Application-specific integrated circuit Amplitude shift keying Automated switched-transport network Array waveguide grating Additive white Gaussian noise Bit error rate Bit-interleaved parity Broadband light source Bandpass filter Binary phase-shift keying Connection controller interface Chromatic dispersion Characteristic information Connection monitoring Constant modulus algorithm Complementary metal-oxide semiconductor Carrier-to-noise ratio Coherent optical OFDM Cyclic prefix Common phase error Continuous-phase frequency-shift keying Carrier-suppressed return to zero Continuous wave Duobinary Differential binary phase-shift keying Distributed Bragg reflector Direct current

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List of Acronyms

DCF DCM DD-LMS DFB DFT DGD DGEF DI DML DOP DPASK DPSK DQPSK DSB DSP DTS DWDM EAM EBPF ECC EDC EDFA EO ESNR FBG FDE FDI FEC FFT FIR FM FOM FROG FSR FWM GVD HNLF IC ICI IEEE IM/DD IP IrDI ISI

Dispersion-compensating fiber Dispersion-compensating module Decision-directed least mean square Distributed feedback Discrete Fourier transform Differential group delay Dynamic gain equalizing filter Delay interferometer Directly modulated laser Degree of polarization Differential phase-amplitude-shift keying Differential phase-shift keying Differential quadrature phase-shift keying Double sideband Digital signal processing Delay-tap sampling Dense wavelength-division multiplexing Electro-absorption modulator Electrical bandpass filter Embedded control channel Electronic dispersion compensation Erbium-doped fiber amplifier Electrical to optical Electrical signal-to-noise ratio Fiber Bragg grating Frequency-domain equalization Forward defect indication Forward error correction Fast Fourier transform Finite impulse response Frequency modulation Figure of merit Frequency-resolved optical gating Free spectral range Four-wave mixing Group velocity dispersion Highly nonlinear fiber Integrated circuit Intercarrier interference Institute of Electrical and Electronic Engineers Intensity modulation/direct detection Internet Protocol Interdomain interface Intersymbol interference

List of Acronyms

ITU LD LEAF LO LOS LP LPF LS LSB MCM MEMS MI MIMO ML MLSE MMSE MPDR MPI MPLS MSPE MTBF MTTR MZ MZI MZM NE NLSE NMS NPN NRZ NZ-DSF OA OADM OAM OBPF OCA OCC OCE OCG OCh OChr OCM ODUk ODUkP

International Telecommunication Union Laser diode Large effective area fiber Local oscillator Linear optical sampling Low pass Low-pass filter Least square Lower sideband Multicarrier modulation Micro-electro-mechanical systems Modulator index Multiple-input and multiple-output Maximum likelihood Maximum likelihood sequence equalizer Minimum mean-square estimation Monitoring power dynamic range Multiple path interference Multiprotocol label switching Multisymbols phase estimation Mean time before failure Mean time to repair Mach-Zehnder Mach-Zehnder interferometer Mach-Zehnder modulator Network element Nonlinear Schro¨dinger equation Network management system Nonlinear phase noise Non-return to zero Nonzero dispersion-shifted fiber Optical amplifier Optical add/drop multiplexer Operations, administration, and maintenance Optical bandpass filter Optical channel analyzer Optical channel carrier Optical channel estimation Optical channel group Optical channel Optical channel with reduced functionality Optical channel monitoring Optical data unit of level k Optical data unit of level k, path

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ODUkT OE OEO OFDM OH OMS ONE ONU OOK OOS OPM OPPL OPS OPSn OPUk OS OSA OSC OSNR OTDR OTM OTN OTR OTS OTU OTUk OTUkV OXC PA PBS PD PDF PDG PDL PDM PHB PM PM PMD PMDC PMF PolMux PON PRBS

List of Acronyms

Optical data unit of level k, tandem connection sublayer Optical-to-electrical Optical-electrical-optical Orthogonal frequency-division multiplexing Overhead Optical multiplex section Optical network element Optical network unit On-off keying Optical transport multiplex overhead signal Optical performance monitoring Optical phase-locked loop Optical physical section Optical physical section of level n Optical channel payload unit of level k Optical section Optical spectrum analyzer Optical supervisory channel Optical signal-to-noise ratio Optical time-domain reflectometer Optical transport module Optical transport network Optical-to-RF Optical transmission section Optical channel transport unit Optical channel transport unit of level k Optical channel transport unit of level k, functionally standardized Optical crossconnect Preamplifier Polarization beam splitter Photodetector Probability density function Polarization-dependent gain Polarization-dependent loss Polarization-division multiplexing Polarization hole burning Polarization maintaining Phase modulator Polarization mode dispersion Polarization mode dispersion compensator Polarization-maintaining fiber Polarization multiplexing Passive optical network Pseudo-random bit sequence

List of Acronyms

PSD PSP PXC QAM QoS QPM QPSK RAH RF RFSA ROADM RSOA RTO RWA Rx RZ S&H SBS SCM SDH SE SES SHG SISO SITO SLA SMF SNR SOA SONET SOP SPIDER SPM SRB SRS SSB SSMF TC TCM TDE TDM TIA TITO TMN

Power spectral density Principal states of polarization Photonic crossconnect Quadrature amplitude modulation Quality of service Quasi phase matched Quadrature phase-shift keying Reference asynchronous histogram Radio frequency RF spectrum analyzer Reconfigurable optical add-drop multiplexer Reflective semiconductor optical amplifier RF-to-optical Routing wavelength assignment Receive Return zero Sample and hold Stimulated Brillouin scattering Subcarrier multiplexing Synchronous digital hierarchy Spectral efficiency Severely errored second Second harmonic generation Single-input and single-output Single-input and two-output Service-level agreement Single-mode fiber Signal-to-noise ratio Semiconductor optical amplifier Synchronous optical network State of polarization Spectral phase interferometry for direct electric-field reconstruction Self-phase modulation Stimulated Rayleigh back-scattering Stimulated Raman scattering Single sideband Standard single-mode fiber Tandem connection Tandem connection monitoring Time-domain equalization Time-division multiplexing Transimpedance amplifier Two-input and two-output Telecommunication(s) management network

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TPA TTI Tx UNI VOA VPN VSB WB WDM XGM XPM xPSK xQAM ZF

List of Acronyms

Two-photon absorption Trail trace identifier Transmit User (to) network interface Variable optical attenuator Virtual private network Vestigial sideband Wavelength blocker Wavelength-division multiplexing Crossgain modulation Crossphase modulation Multilevel phase-shift keying Multilevel quadrature amplitude modulation Zero forcing

List of Figures FIGURE FIGURE FIGURE FIGURE FIGURE

1.1 1.2 1.3 1.4 1.5

FIGURE 1.6 FIGURE 1.7 FIGURE 1.8 FIGURE 2.1 FIGURE 2.2 FIGURE 2.3 FIGURE 2.4 FIGURE 2.5


Window of operability is shrinking as networks become more complex. A self-managed network with optical performance monitoring. Features of ubiquitous monitoring for robust and self-managed networks. Overview of various optical impairments within the network. Future heterogeneous networks should accommodate various types of traffic and use optimal channel characteristics for each application/user. The required hardware should be reconfigurable and transparent. Multivariable routing. (a) Denial of service, and (b) eavesdropping of an intrusive wavelength using the nonlinear effects in the fiber link. Identification and localization of network impairments allow network resources to be adapted for compensation, data re-routing, and resource reallocation. Graphical description of OSNR measurement based on optical spectrum. (a) Conceptual diagram of optical spectrum analyzer. (b) Relationship between level of optical noise and resolution bandwidth (BW) of OSA. Out-of-band noise measurement using AWG. Out-of-band noise measurement using OSAs, when there are DGEs within the optical link. The OSAs measure the power of out-of-band ASE noise for the OSNR monitoring, as well as the channel powers for the gain equalization. (a) Example of a dynamically reconfigurable transparent optical network configured with ROADMs. Optical spectrum measured at point A, (b) when unmodulated CW signals are transmitted, or (c) when 10-Gb/s NRZ signals are transmitted. (Resolution bandwidth: 0.05 nm.) (a) Optical spectrum of 43-Gb/s RZ-DQPSK signals. (b) Optical spectrum measured after turning off modulators. (Resolution: 0.1 nm; div, division.) Principle of OSNR monitoring based on polarization-nulling technique. Copyright # 2006 IEEE. Configuration of polarization-nulling technique based on (a) adaptive polarization control with feedback, or (b) rotating quarter-wave plate and polarizer. (a) Correlation between DOP and OSNR. (b) OSNR monitoring sensitivity to DOP error. Measured DOP and normalized power of WDM signals. Copyright # 2006 IEEE. Illustration of the error mechanism caused by PMD in the execution of the polarization-based OSNR monitoring technique in (a) the time domain and (b) the frequency domain. OSNR monitoring error caused by PMD (mean PMD = 3.22 ps). Copyright # 2001 IEEE. Illustration of error mechanism caused by nonlinear birefringence in execution of polarization-based OSNR monitoring technique.

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List of Figures

FIGURE 2.14

FIGURE 2.15 FIGURE 2.16

FIGURE 2.17 FIGURE 2.18 FIGURE 2.19 FIGURE 2.20 FIGURE 2.21 FIGURE 2.22 FIGURE 2.23 FIGURE 2.24 FIGURE 2.25 FIGURE 2.26 FIGURE 2.27 FIGURE 2.28 FIGURE 2.29

Maximum OSNR errors caused by nonlinear birefringence (a) measured in a twochannel experiment (200-GHz spacing, and 0- and 7-dBm input power for the probe and pump, respectively) (Copyright # 2006 IEEE), or (b) measured in various 640-km-long fiber links with six channels (200-GHz spacing, 0-dBm/ channel input power, 80 km 8 spans) (Copyright # 2001 IEEE). Illustration of the error mechanism caused by PDL in execution of polarizationbased OSNR monitoring technique. (a) Cumulative probability of errors in the measured OSNRs by using the polarization-nulling technique due to partially polarized ASE noise in a transmission link consisting of 15 spans (average PDL/span = 0.57 dB). (b) Probability that the error in the measured OSNRs by using the polarization-nulling technique becomes >1 dB (due to the partially polarized ASE noise caused by PDL). Copyright # 2006 OSA. (a) Fourier components of the Stokes parameters of an optical signal measured in a 120-km-long aerial fiber link. (b) OSNR measured by using the polarizationnulling technique in a 120-km-long aerial fiber link. Copyright # 2004 OSA. Schematic diagram of the polarization-nulling technique improved by using additional optical filter. PBS, polarization beam splitter; BPF, bandpass filter; PD, photo detector. Copyright # 2006 IEEE. (a) Illustration of off-center filtering technique for OSNR monitoring. (b) Effects of filter detuning for 39.81-Gb/s, 2.5-ps, full-width at half-maximum (FWHM) RZ signal. Copyright # 2004 IEEE. Schematic diagram of the polarization-nulling technique, improved by multiplefrequency measurement and PMD compensation. Copyright # 2006 IEEE. Principle of spectral SOP measurement technique. (a) Schematic diagram of OSNR monitoring technique based on MZDI interferometer. (b) Total power measurement with constructive interference. (c) Noise power measurement with destructive interference. Schematic diagram of modified optical interferometer for OSNR monitoring. (a) Power spectral density of receiver noises caused by signal-ASE and ASE-ASE beating. (b) Measured receiver noise spectrum from 40 to 50 kHz when OSNR is 20 dB or 30 dB. Principle of OSNR monitoring technique based on low-frequency beat noise analysis. ADC, analog-to-digital converter; B, bit rate; FFT, fast Fourier transform; PD, photodetector. RF spectrum of 10-Gb/s NRZ signal with PRBS pattern (pattern length: 223 – 1). (a) Principle of OSNR monitoring technique based on high-frequency beat noise analysis. (b) RF spectrum of 2.5-Gb/s NRZ signal with 20-dB or 30-dB OSNR. OSNR of 2.5-Gb/s NRZ signal measured by monitoring beat noise at (a) 2.5-GHz null point or (b) 10 GHz. Schematic diagram of OSNR monitoring technique based on polarization diversity.

List of Figures


FIGURE 2.43

FIGURE 2.44 FIGURE 3.1 FIGURE 3.2

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Waveforms of 1.25-Gb/s signal. The upper graph shows the outputs of two arms of the polarization-diversity receiver; the lower graph shows the sum and difference of outputs. Copyright # 2005 IEEE. Conversion from polarization variation to intensity variation by polarizationdiversity receiver with subtraction circuit. Schematic diagram of orthogonal polarization delayed-homodyne technique for monitoring OSNR. (a) Principle of nullifying data spectrum using the orthogonal polarization delayedhomodyne technique. (b) Measured RF spectra of 10-Gb/s NRZ signal with and without data spectrum nullifying. Schematic diagram of OSNR monitoring technique based on frequency diversity. RF spectra of the signal after (a) one photodiode and (b) balanced subtraction. Copyright # 2005 IEEE. Effect of chromatic dispersion on OSNR monitoring technique based on a frequency-diversity receiver. Copyright # 2008 IEEE. Schematic diagram of orthogonal polarization self-heterodyne technique for monitoring OSNR. Operating principle of orthogonal polarization self-heterodyne technique for OSNR monitoring. (a) Relation of polarization states of the signals in branches 1 and 2. (b) Effect of PMD on electrical spectrum. Copyright # 2007 IEEE. (a) Schematic diagram of OSNR monitoring technique based on synchronously gated signal. RF spectra of signal (b) without gating pulses and (c) with gating pulses. Copyright # 2006 OSA. Schematic diagram of OSNR monitoring technique based on operating condition of optical amplifiers. OCM, optical channel monitor; PM, power monitor. Copyright # 2008 IEEE. (a) Schematic diagram of OSNRlink monitoring. (b) Schematic diagram of OPM manager GUI, graphic user interface; LSP, label-switched path; CLI, command line interface; GMPLS, generalized multiprotocol label switching; SNMP, Simple Network Management Protocol. Copyright # 2009 IEEE. Monitoring error of link OSNR caused by total power monitoring (a) without calibration and (b) with 50% of optical noise power calibrated (contour plots). Span loss = gain of EDFA = 20 dB; noise figure of EDFA = 8 dB; optical noise bandwidth = 30 nm; input power/channel to fiber = 0 dBm. Copyright # 2009 IEEE. Maximum OSNR monitoring error in the worst-case scenario for optical link with AGC-EDFAs (a) under normal operating conditions, or (b) with an increase of 10 dB in span loss at worst position (contour plots). Copyright # 2009 IEEE. Dispersion coefficient, D, as a function of wavelength in conventional silica single-mode fiber. Copyright # 2003 IEEE. Dispersion coefficient in a dispersion-shifted fiber.

xxiv

List of Figures



FIGURE 3.7


FIGURE 3.10 FIGURE 3.11 FIGURE 3.12 FIGURE 3.13 FIGURE 3.14 FIGURE 3.15

CD values for several commercially available types of transmission fiber. Origin of CD in data transmission. (a) CD is caused by the frequency-dependent refractive index in fiber. (b) Non zero spectral width due to data modulation. (c) Dispersion leads to pulse broadening, proportional to the transmission distance and data rate. f, frequency; v, velocity. Transmission distance limitations due to uncompensated dispersion in SMF as a function of data rate for intensity-modulated optical signals. Copyright # 2001 IEEE. (a) The glass that a photon in the l3 pulse “sees” changes as other channels (with potentially varying power) move to coincide with the l3 pulse. (b) System performance (SNR) versus fiber dispersion. Higher dispersion is preferred to reduce XPM effects. Copyright # 1994 IEEE. (a, b) FWM induces new spectral components via nonlinear mixing of two wavelength signals. (c) The signal degradation due to FWM products falling on a third data channel can be reduced by even small amounts of dispersion. Copyright # 1995 IEEE. Dispersion map of basic dispersion-managed system. Positive dispersion transmission fiber alternates with negative dispersion compensation elements such that total dispersion is nearly zero end to end. (a) Zero-dispersion wavelength shifts due to temperature change; thus, dispersion itself changes at a fixed wavelength (b) For a 40-Gb/s, 1000-km fiber link, 30 C temperature change causes dispersion beyond system limit. Copyright # 2000 IEEE. Principle of RF fading used for dispersion monitoring: RF tone within data band fades due to CD. Copyright # 2002 IEEE. (a) RF fading due to dispersion for 7- and 9-GHz tones. Solid lines represent theoretical results and points are experimental results. (b) Theoretical curve for measurable range of CD. Copyright # 2002 IEEE. (a) Clock regenerating effect for NRZ data. (b) Clock fading effect for RZ data. Solid lines represent without SPM; dashed lines represent with SPM; dotted lines represent experimental. Copyright # 2001 IEEE. (a) Experimental setup. RF power at half of data rate (5 GHz) is measured after MZI with path difference of 100 ps. (b) Received RF power as function of dispersion. Copyright # 2005 IEEE. Conceptual diagram for monitoring CD using optical VSB filtering. Recovered bits from either part of spectrum arrive at slightly different times depending on CD. Copyright # 2002 IEEE. Phase shift between the two VSB signals versus the normalized. The three lines are simulation results for NRZ data, Gaussian filter (dashed dotted line); RZ data, Gaussian filter (solid line); and RZ data, fiber Fabry-Perot filter (dashed line). Scatter points are experimental for 10-Gb/s RZ data using a fiber Fabry-Perot filter. Copyright # 2002 IEEE.

List of Figures



FIGURE 3.22 FIGURE 3.23 FIGURE 3.24 FIGURE 3.25 FIGURE 3.26 FIGURE 3.27 FIGURE 4.1 FIGURE 4.2 FIGURE 4.3 FIGURE 4.4 FIGURE 4.5 FIGURE 4.6

xxv

(a) Eye diagram and (b) histogram with synchronous sampling. (c) Eye diagram and (d) histogram with asynchronous sampling. Copyright # 2004 IEEE. Portraits processing of delay-tap sample pairs to create phase portraits. Labels on phase portrait represent the sampled bit sequences. Copyright # 2007 IEEE. Eye diagrams and phase portraits for NRZ: (a) OSNR = 35 dB and no impairment; (b) OSNR = 25 dB; (c) OSNR = 35 dB and CD = 800 ps/nm; (d) OSNR = 35 dB and PMD = 40 ps; (e) OSNR = 35 dB and crosstalk = 25 dB; and (f) OSNR = 25 dB, CD = 800 ps/nm, PMD = 40 ps, and crosstalk = 25dB. Copyright # 2007 IEEE. Monitor versus actual values of various impairments and signal quality measures for simultaneous mixtures of OSNR, CD, PMD, and filter offset from 10-Gb/s NRZ simulations. Copyright # 2007 IEEE. Principle of residual dispersion monitoring via SPM and filtering. Copyright # 2002 IEEE. Comparison of long-pass, spectral monitoring signal (solid squares) with typical 40-Gb/s RZ receiver BER penalty (open circles), both plotted versus residual dispersion. Eye diagrams are indicated for three residual dispersion values. Lines are a guide to the eye. Copyright # 2002 IEEE. Experimental setup to vary noise and accumulated dispersion on a data signal. The wavelength-converted monitoring signal is generated by mixing Pdata with CW signal PCW in SOA and selected by the optical filter. Copyright # 2005 IEEE. Monitor signal for 40-Gb/s data as function of accumulated dispersion. Copyright # 2005 IEEE. (a) Typical measured data for logarithm of BER versus decision threshold (Copyright # 1993 IEEE). (b) BER as a function of the received optical SNR (Copyright # 1988 Holt, Rinehart, and Winston). RF tone fading due to CD and PMD. System setup of the CD monitoring scheme suppressing PMD and chirp effects. Copyright # 2006 IEEE. CD monitoring error, (a) versus DGD without and with PMD cancellation, and (b) versus a parameter without and with chirp suppression. Copyright # 2006 IEEE. Origin of PMD. Illustration of input optical pulse with power transmitted on the two PSPs, each arriving at a different time. (a) Probability distribution of DGD in typical fiber. (b) System performance (BER) fluctuations due to changes in temperature caused by PMD. Copyright # 1991 IEEE. Graphical representation of all-order PMD effect on an optical pulse. Transmission distance limitations for a 40-Gb/s NRZ system due to combination of fiber PMD and PMD of cascaded in-line optical components found in amplifier sites. Copyright # 2004 IEEE/OSA. Explanation of PMD-induced RF power fading in an SSB SCM system in optical domain. Copyright # 2004 IEEE.

xxvi

List of Figures


FIGURE 4.15


FIGURE 4.19

FIGURE 4.20

Received RF power variation versus DGD for eighth, quarter, half, and bit rate frequency components. Copyright # 2004 IEEE/OSA. Concept of CD-insensitive RF power fading using optical bandpass filtering. Copyright # 2004 IEEE. CD-induced RF clock power fading under various DGD values (a) without bandpass filtering and (b) with bandpass filtering. Insets are RF clocks when DGD is 23 ps and CD is 0 and 640 ps/nm, respectively. Copyright # 2004 IEEE. Experimental setup of simultaneous PMD and OSNR monitoring through enhanced RF spectrum analysis by adding large DGD element. FMLL, fiber mode–locked laser. Copyright # 2005 IEEE. PMD monitoring results for 10-Gb/s, 2.5% RZ data by adding large DGD element. OSNR varies from 15 to 35 dB. Copyright # 2005 IEEE. (a) Conceptual diagram of PMD monitoring technique for DPSK/DQPSK. (b) RF power increases with decreasing FSR of polarization-based interferometer filter (i.e., with increasing DGD values). Copyright # 2008 IEEE. Experimental results of (a) RF power measured at 170 MHz for PMD monitoring of NRZ-DQPSK and NRZ-DPK, and (b) CD dependence for PMD monitoring with DGD 23 ps and 40 ps, respectively. Copyright # 2008 IEEE. Schematic illustration of DOP signal degradation by PMD. (a) Optical waveform and SOP of signal without PMD. (b) Optical waveform and SOP of signal with PMD. The x and y axes correspond to two PSPs of the transmission media. Copyright # 2001 IEEE/OSA. DOP as function of DGD for 10-Gb/s NRZ data modulated by MZ modulator. Plots, experiment; dashed line, rectangular waveform approximation; thin lines, numerical simulation. All simulated DOP curves are relatively unaffected by chirp parameter a and by fiber dispersion of 350 ps/nm. Copyright # 2001 IEEE/OSA. Theoretical results of minimum DOP versus DGD (relative to bit time, Tb) as pulse width of RZ signal varies. Copyright # 2004 IEEE/OSA. Sensitivity of DOP reduction as a function of DGD (first-order PMD). Copyright # 2001 IEEE/OSA. Measured DOP reduction with scrambled input polarization of 40-Gb/s RZ signal. (a) First-order PMD of 1.25 ps. (b) Second-order PMD, concatenation of two unaligned birefringent secitons (6-ps and 4-ps DGD). Copyright # 2001 IEEE/ OSA. Prior to optical filtering, an RZ signal that undergoes DGD equal to the pulse width is completely deplorized, thus limiting the DGD monitoring range of DOPbased DGD monitors. After filtering, the signal is partially polarized, allowing DOP-based monitoring of the DGD. Copyright # 2001 IEEE/OSA. Frequency-domain illustration of reducing depolarization via symmetric narrowband optical filtering. Short optical pulses have a wide optical spectrum, enhancing the effects of DGD-induced depolarization. A narrowband filter shrinks the optical spectrum, thus reducing these depolarization effects and increasing the DGD monitoring range. Copyright # 2001 IEEE/OSA.

List of Figures

FIGURE 4.21

FIGURE 4.22 FIGURE 4.23 FIGURE 4.24 FIGURE 4.25 FIGURE 4.26 FIGURE 4.27 FIGURE 5.1 FIGURE 5.2 FIGURE 5.3 FIGURE 5.4 FIGURE 5.5

FIGURE 5.6 FIGURE 5.7 FIGURE 5.8 FIGURE 5.9 FIGURE 5.10 FIGURE 5.11 FIGURE 5.12

xxvii

(a) Experimental results for minimum DOP versus DGD for 40-Gb/s 50% RZ signals. (b) Simulation results for minimum DOP versus DGD for 40-Gb/s NRZ signals before and after asymmetric partial optical filtering. Copyright # 2001 IEEE/OSA. (a) Design of error monitor with analog integrator, and (b) typical characteristics of integrator voltage Uint versus monitor threshold U1 for first-order PMD signals with variable DGD and Y = 0.5. Copyright # 2001 IEEE/OSA. (a) BER versus eye opening for all orders PMD statistics. (b) BER after compensation versus eye opening using eye monitoring. Copyright # 2001 IEEE/OSA. (a) Eye diagram and (b) histogram with synchronous sampling. (c) Eye diagram and (d) histogram with asynchronous sampling. Copyright # 2004 IEEE/OSA. Portrait processing of delay-tap sample pairs to create phase. Labels on phase portrait represent sampled bit sequences. Copyright # 2007 IEEE/OSA. Eye diagrams and phase portraits for NRZ with OSNR = 35 dB at (a) no impairment and (b) PMD = 40 ps. Copyright # 2007 IEEE/OSA. Measurement of effective DGD (root mean square error of 3.1 ps) in presence of OSNR levels ranging from 13.5 to 25 dB. Copyright # 2009 IEEE/OSA. The effect of timing misalignment between pulse carver and data modulator. Copyright # 2003 IEEE. Measured spectrum asymmetry due to timing misalignment. Copyright # 2002 OSA. Measured spectra for (a) aligned and (b) misaligned cases. The first spectral null occurs at around 6.4 GHz. Copyright # 2003 IEEE. (a) Simple alignment-detection scheme. (b) Measurements: microwave monitoring of misalignment. Copyright # 2003 IEEE. (a) Illustration of timing alignment between pulse carver and data modulator. (b) Calculated signal spectra with timing alignment between pulse carver and data modulator of (i) 0, (ii) 0.3, and (iii) 0.5 T in a 10-Gb/s RZ-DPSK system with 0.28-T pulsewidth. Copyright # 2005 IEEE. (a) Proposed setup for monitoring clock misalignment. (b) Frequency-to-intensity conversion characteristic of delay-and-add discriminator. Copyright # 2006 IEEE. (a) Two-tap plot for various modulation timing misalignment. (b) Measured d and t parameters for various timing misaligments. Copyright # 2006 IEEE. Misalignment monitoring of an RZ-DQPSK transmitter. Copyright # 2008 IEEE. Monitoring signal power for (a) I/Q data misalignment and (b) carver/data misalignment. Copyright # 2008 IEEE. (a) Basic structure of CPFSK modulator with synchronous control. Numerically calculated optical modulation spectra for (b) CPFSK and (c) BPSK (DPSK). Copyright # 2006 IEEE. Modulation characteristics of PM and MZM. Copyright # 2006 IEEE. Receiver power penalty versus t0 for various ASK formats and DQPSK modulation methods. (a–c) sampling phases are optimized. Copyright # 2006 IEEE.

xxviii

List of Figures


FIGURE 6.3



FIGURE FIGURE FIGURE FIGURE

6.12 6.13 6.14 6.15

(a) Synchronous and (b) asynchronous eye diagrams and corresponding histograms of an NRZ signal. Examples of asynchronous histograms acquired from an NRZ signal (a) in presence of ASE noise for different values of signal-to-noise ratio; (b) in presence of intraband crosstalk; and (c) when the signal is impaired by fiber dispersion. Copyright # 2009, Institution of Engineering and Technology. Examples of histogram acquisition systems. (a) Based on electrical sampling using an unsynchronized digital oscilloscope (# 2009, IEEE). (b) Based on optical sampling using a sum-frequency generation crystal. Copyright # 2009, Institution of Engineering and Technology. Schematic of asynchronous histogram for an average Q-factor analysis technique. Copyright # 2009, Institution of Engineering and Technology. Relationship between Q-factor and average Q-factor using analysis of an NRZ signal. ○, a = 0.1; x, a = 0.2; , a = 0.3; □, a = 0.4; þ, a = 0.49. Copyright # 2009, Institution of Engineering and Technology. Relationship between Q-factor and average Q-factor using analysis of an NRZ signal for different values of dispersion-impairing 10-Gb/s signal under analysis. ○, 0 ps/nm; x, 1190 ps/nm; , 1530 ps/nm. Copyright # 2009, IEEE. Schematic of histogram crosspoint elimination method. Copyright # 2009, Institution of Engineering and Technology. Illustration of BER estimation method using multi-Gaussian fitting of asynchronous histogram after process to eliminate crosspoint data. Copyright # 2009, Institution of Engineering and Technology. Histogram of mark symbol of signal affected by intraband crosstalk (signal-tocrosstalk ratio of 19 dB) and degraded by Gaussian noise (a) without deconvolution and (b) after deconvolution and filtering. In the case of (b), the crosstalk floor around the mark symbol becomes clearly visible and may be evaluated. Copyright # 2009, Institution of Engineering and Technology. Diagram of method to estimate an asynchronous histogram of the signal impaired with the ASE noise proposed. Asynchronous histogram acquired from simulated signals with □, raised-cosine pulse shape; •, rectangular pulse shape filtered by Bessel filter with bandwidth of 70% of signal’s bit rate; , 40-Gb/s raised-cosine signal degraded by accumulated chromatic dispersion of 34 ps/nm; continuous lines, estimated histograms using the raised-cosine approximation. Copyright # 2009, IEEE. Diagram of histogram estimation method A. Diagram of histogram estimation method B. Schematic diagram of OMS for OSNR evaluation using asynchronous histograms. Asynchronous histograms of reference signal considering an OSNR of 28 dB (dashed line) and signal under analysis with OSNR values of 14, 20, and 30 dB (continuous lines) for (a) power at OMS input of 0 dBm and (b) power at OMS input of 5 dBm. Optical preamplification in OMS is not considered in this case. Copyright # 2009, IEEE.

List of Figures

FIGURE 6.16

FIGURE 6.17


FIGURE 6.21 FIGURE 6.22 FIGURE 6.23 FIGURE 6.24


FIGURE 7.5

FIGURE 7.6

xxix

Dependence of estimated OSNR on OSNRSIG for OSNRREF = 28 dB (□), OSNRREF = 22 dB (), and OSNRREF = 16 dB (○). Optical amplification or filtering in OMS is not considered in this case. The insets present eye diagrams of signal under analysis for OSNR values of 16 and 30 dB. (a) Power at OMS input of 0 dBm and (b) power at OMS input of 5 dBm. Copyright # 2009, IEEE. Dependence of estimated OSNR on OSNRSIG for OSNRREF = 28 dB (□), OSNRREF = 22 dB (), and OSNRREF = 16 dB (○). An EDFA is used within the OMS for pre-amplification. The insets present the eye diagram of the signal under analysis for OSNR values of 16 and 30 dB. (a) Power at OMS input of 20 dBm and (b) power at the OMS input of 25 dBm. Copyright # 2009, IEEE. Simplified block diagram of S&H circuit. General block diagram of S&H system. Copyright # 2009, IEEE. Signal-to-sampling-noise ratio as function of aperture time for different values of transition time. (a) Using rectangular sampling impulse. (b) Using triangular sampling impulse. Numerical simulation results, symbols; analytical results, continuous line. General block diagram of equivalent S&H system. Copyright # 2009, IEEE. Simulated nonideal histogram acquisition system for optical monitoring. qeq/q and qcor/q as function of aperture time for 40-Gb/s signal. •, qeq/q for q = 8; ○, qeq/q for q = 10; n, qcor/q for q = 8; □, qcor/q for q = 10. Copyright # 2009, IEEE. Asynchronous histogram of a 40-Gb/s signal obtained with a nonideal sampling system from simulated signals with □, aperture time of 28 ps; •, aperture time of 44 ps. Continuous lines represent estimated histogram. Arrows indicate new relative maximums that result from averaging effect induced by nonideal sampling. Copyright # 2009, IEEE. Schematic of asynchronous delay-tap sampling technique. Sample pairs are separated by a fixed delay, t. NRZ phase portraits for (a) 1-bit delay, and (b) ¼-bit delay. Phase portraits of an NRZ signal showing the effects of small changes in tap delay. Eye diagrams and phase portraits (1-bit delay, ¼-bit delay) for 10-Gb/s NRZ: (a) OSNR = 35 dB and no impairments; (b) OSNR = 25 dB; (c) CD = 800 ps/nm; (d) DGD = 40 ps; (e) crosstalk = 25 dB; and (f) OSNR = 25 dB, CD = 800 ps/nm, PMD = 40 ps, and crosstalk = 25 dB. Setup for generation of training sets. The impairment emulator adds known combinations of OSNR, CD, and first-order PMD to clean transponder signals. A polarization controller ensures a random distribution of power splits between principal states. AS, asynchronous sampler and outboard processing; PC, polarization controller; TDCM, tunable dispersion compensation module; TOF, tunable optical filter; Tx, transponder under test; VOA, variable optical attenuator. Experimental phase portraits for (a–c) 10-Gb/s NRZ and (d–f) 40-Gb/s DPSK showing the effects of CD and DGD; the OSNR is 14 dB for all cases. The tap

xxx

List of Figures

FIGURE 7.7

FIGURE 7.8

FIGURE 7.9


FIGURE 8.1

FIGURE 8.2

FIGURE 8.3

delay was set at 25 ps for both bit rates. The impairment levels were DGDeff (ps), absCD (ps/nm): (a) 0,0, (b) 39,0, (c) 0,1200, (d) 0,0, (e) 14,0, and (f) 2,400. Experimental measurements (gray) of simultaneous OSNR, CD, and DGD impairments for (a–c) 10-Gb/s NRZ and (d–f) 40-Gb/s NRZ-DPSK. Results for 250 test cases are ordered along the x axis by true values (shown in black). The test errors, stdTe, are quoted at the 2s level. Setup for 10-G NRZ WDM system test. OSNR and CD were monitored at the three tap points. An additional 400 ps/nm dispersion was added at each of the tap points as a further check of CD accuracy. The input power to the monitor was 18 dBm. Simulation results for 10-Gb/s NRZ demonstrating monitoring of simultaneous OSNR, CD, DGD, in-band crosstalk, and optical filter offset. The training ranges were for OSNR, 11–25 dB; CD, 1400 to 1400 ps/nm; and DGD, 0–50 ps (random g), crosstalk 15–24 dB, and filter offset 0–12 GHz. The training set consisted of 2000 random combinations of these impairments. Predictions for 1000 test cases (gray) are shown; true values are shown in black. The RMS error at the 2s level are (a) OSNR 0.3 dB, (b) absCD 15 ps/nm, (c) DGD 1.6 ps, (d) crosstalk 1.4 dB, and (e) filter offset 0.7 GHz. Simulation results for simultaneous measurements of absCD and DGDeff for 40Gb/s RZ-DQPSK. The training set contained 2000 cases with OSNR ranging from 14 to 28 dB; CD, 800 to 800 ps/nm; and DGD, 0 to 25 ps. Simulated phase portraits for (a–c) 40-Gb/s RZ-DQPSK and (d–f) 80-Gb/s polarization-multiplexed DPSK showing the effects of CD and DGD. The OSNR is 14 dB for all cases. The tap delay was set at 25 ps for both bit rates. The impairment levels were DGDeff (ps), absCD (ps/nm): (a) 0,0, (b) 25,0, (c) 0,800, (d) 0,0, (e) 25,0, and (f) 0,800. Data-encoded optical signals represented by intensity and phase as a function of time (left column) and complex electric field at center of time slot (right column). The signals correspond to (a) on-off keying, (b) binary phase-shift keying, (c) quaternary phase-shift keying, and (d) 16-state quadrature amplitude modulation. Layout for the direct measurement of the complex interference between data source and local oscillator. The two sources are split at the splitters SLO and SDATA, and recombined pairwise at the combiners CA and CB. Balanced photodetection of the two outputs of each combiner yields the in-phase and quadrature components of the interference if a relative p/2 phase shift is introduced in the optical path of one of the two sources between splitters and combiners. Copyright # 2006 OSA. Spectral representation of the sources involved in various implementations of linear optical sampling. (a) the Monochromatic local oscillator is spectrally overlapped with the signal. (b) the Broadband pulsed local oscillator is overlapped with the signal. (c) the Nonlinear interaction between the short pump pulse and the signal leads to an idler pulse, and the monochromatic signal is overlapped with the generated idler.

List of Figures


FIGURE 8.6

FIGURE 8.7

FIGURE 8.8

FIGURE 8.9

FIGURE 8.10

FIGURE 8.11

FIGURE 8.12

xxxi

Train of sampling pulses in the time domain showing the carrier-phase evolution under the field envelope. Phase samples measured on a 10-Gb/s BPSK signal. The phase is shown (a) without any processing, (b) after removal of a linear term leading to rotation in complex plane, and (c) after removal of both a linear term and a slowly-varying phase. Schematic of a polarization interferometer. The orthogonally polarized data and sampling sources are combined with a nonpolarizing 3-dB coupler. The two outputs of the coupler are sent to identical setups leading to a pair of balanced photodetectors, excepting that a p/2 phase shift is introduced in one arm, so that the real and imaginary parts of the interference are measured. (a) Picture of a 90-degree optical hybrid made with silicon-on-silica. (b) Setup for coherent photodetection of a data source with a copolarized pulsed local oscillator using the waveguide 90-degree optical hybrid. The sampling source is spectrally filtered to match the optical spectrum of the data source. The relative phase between the two measured signals is controlled by applying a low continuous voltage to a thermo-optic coupler. Copyright # 2005 OSA. Setup for the coherent photodetection of a data source by gating with four-wave mixing and detection with a monochromatic local oscillator. The sampling and data source are combined and propagate in a highly nonlinear fiber. The idler resulting from four-wave mixing of the sampling source acting as a pump on the data source is filtered and detected by homodyne detection with a local oscillator. Courtesy of Mathias Westlund and Peter A. Andrekson; copyright # 2009 OSA. Experimental results obtained with four-wave mixing and coherent detection. (a) Constellation diagram of 625,000 samples measured on a 10-GBaud QPSK signal. Electric-field samples located at the center of the bit slots in a time interval corresponding to 20% of the bit period are plotted in black. Other samples appear in gray, while lines correspond to interbit transitions averaged over a large number of similar transitions in the bit sequence. (b) Measured phase as function of position in the bit sequence. (c) Close-up of (b) in a 10-bit intervals. Courtesy of Mathias Westlund and Peter A. Andrekson; copyright # 2009 OSA. Constellation diagrams measured (a) on an ASE source, (b) on a monochromatic source with an OSNR of 15.8 dB, (c) on a binary PSK signal with an OSNR of 18 dB, and (d) on a binary PSK signal with an OSNR of 13 dB. (e) Measured variance of one of the symbols along the real and imaginary axes s2x and s2y versus the measured OSNR. Line of dots represents the theoretical relation between variance and OSNR. Copyright # 2006 OSA. (a, b) Constellation diagrams of phase-modulated signals generated with a phase modulator for differential phase of p/2 and p. (c) Standard deviation of the amplitude s and phase sc of one of the symbols as a function of the differential phase (respectively round markers and square markers). Copyright # 2006 OSA. (a, b) Constellation diagrams of BPSK signal generated with Mach-Zehnder modulator for two different amplitudes of the drive voltage. (c) Standard deviation

xxxii

List of Figures

FIGURE 8.13

FIGURE 8.14

FIGURE 8.15

FIGURE 8.16

FIGURE 8.17

FIGURE 8.18


of the amplitude and phase of one of the symbols as a function of the drive voltage (respectively round markers and square markers). Copyright # 2006 OSA. Constellation diagrams measured at input and output of wavelength converter set to reduce the phase noise of a BPSK signal. (a) and (c) are measured before the wavelength converter, while (b) and (d) are the corresponding converted signals. Copyright # 2008 IEEE. Constellation diagrams measured after propagation of a noisy signal in a highly nonlinear fiber at (a) low power and (b) high power. The coupling between the intensity and phase of the samples is a sign of Gordon-Mollenauer phase noise. This coupling is quantified in (c) as a function of the average power of the source. Copyright # 2006 OSA. (a) Constellation diagram of a QPSK signal measured after two roundtrips in a recirculating loop (the propagation distance in transmission fiber is 800 km). Gordon-Mollenauer phase noise can be seen. (b) Coupling between intensity and phase in constellation diagrams measured for various propagation distances and/or launch powers. Measured relative instantaneous power and phase of an amplitude-modulated optical source after propagation in a nonlinear fiber. The coupling between power and phase is due to self-phase modulation, and the nonlinear coefficient of the fiber can be quantified with these data. Copyright # 2005, Institution of Engineering and Technology. Examples of measured constellation diagrams of a 10.7-Gb/s BPSK signal (a) after the transmitter, and (b) after propagation in 90 km of standard single-mode fiber. The trajectories of the electric field in the complex plane are plotted with continuous gray lines and the values of the field at the center of the bit slot are plotted with a black round marker. Courtesy of Michael G. Taylor. (a) Temporal transmission and phase of a semiconductor optical amplifier depleted by a short optical pulse. (b) Temporal intensity and phase of an optical pulse carved by an electro-absorption modulator. In (a), the period of the depleting pulse is 100 ps, while in (b), the period of the drive voltage is 25 ps. The lines correspond to the quantities measured with linear optical sampling and the markers correspond to the same quantities measured with the spectrogram technique. Copyright # 2005 OSA. Pilot-tone-based optical performance monitoring technique. Pilot-tone generation and detection methods. (a) Adding a small sinusoidal current to the laser’s bias current. (b) Dithering bias voltage of external modulator. (c) PM tone generation by using phase modulator. (d) Pilot-tone detection using FFT. (e) Using tunable electrical bandpass filter. (f) Using tunable local oscillator for the down-conversion of tone frequency. LD, laser diode; AM, amplitude modulator; PM, phase modulator; PD, photodetector; A/D, analog-to-digital converter; FFT, fast Fourier transform; BPF, tunable bandpass filter; RFD, radio frequency power detector; LOSC, tunable local oscillator.

List of Figures

FIGURE 9.3

FIGURE 9.4 FIGURE 9.5 FIGURE 9.6 FIGURE 9.7 FIGURE 9.8 FIGURE 9.9 FIGURE 9.10

FIGURE 9.11



9.18 9.19 9.20 9.21

xxxiii

Effects of modulation index and frequency of pilot tone on 10-Gb/s NRZ signal (pattern length = 231 1). (a) Pilot-tone-induced power penalty measured at low tone frequencies. (b) Maximum allowable modulation indices of high-frequency pilot tones for 0.5-dB penalty. Effect of highpass filter on data signal. Eye closure penalty calculated while varying low cut-off frequency in comparison with simulation results (bit rate = 2.5 Gb/s, pattern length = 27 1). Experimental setup. Power penalty measured while varying the bit rate (low cut-off frequency = 1 MHz). Power penalty measured while varying tone frequency (bit rate = 2.5 Gb/s, pattern length = 27 1, MI = 12%). Mechanisms of performance degradation caused by XGM and SRS. Measured optical and electrical spectra. (a) Optical spectrum measured after 640km transmission. (b) Electrical spectrum measured after 640-km transmission (without using control channel). (c) Electrical spectrum measured after first EDFA (with using control channel). (d) Electrical spectrum measured after 640-km transmission (with using control channel). Effects of ghost tones on pilot-tone-based monitoring technique. (a) WDM signals with corresponding pilot tones. (b) Ghost tones generated by XGM and SRS. (c) Pilot tones measured without using a demultiplexing filter. (d) Pilot tones measured after demultiplexing WDM channels. Scalability of pilot-tone-based monitoring technique. Solid line, limits imposed by SRS; dashed line, limits imposed by XGM. Principle of tone-based channel power and wavelength monitoring technique. (a) Experimental setup. (b) Imaginary part of FFT-converted data. (c) Real part of FFT-converted data. Monitoring errors of 16 WDM channels: (a) channel power and (b) wavelength. Experimental setup used for demonstration of pilot-tone-based monitoring technique for optical path and crosstalk. Electrical spectra of pilot tones measured at the output of the switch for l1 in OXC 4 (a) under normal operation, and (b) under switch failure condition. (a) Schematic representation of OADM implemented with the pilot-tone-based monitoring technique. (b) Schematic representation of OXC implemented with the pilot-tone-based monitoring technique. OSW, optical switch; FPF, Fabry-Parot filter; PD, photodetector; Tx, transmitter; Rx, receiver; ADC, analog-to-digital converter; FFT, fast Fourier transform. Operating principle of monitoring technique based on PM pilot tones. Experimental setup of PM pilot-tone-based monitoring technique. Amplitudes and ratio of AM components measured at two adjacent AWG ports. Frequency monitoring error measured while varying tone frequency (accumulated dispersion = 1000 ps/nm) when the WDM channel was operated at 192.8 THz.

xxxiv

List of Figures

FIGURE 9.22 FIGURE 9.23 FIGURE 9.24 FIGURE 9.25 FIGURE 9.26 FIGURE 9.27 FIGURE 9.28 FIGURE 9.29 FIGURE 9.30 FIGURE 9.31 FIGURE 9.32 FIGURE 9.33 FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE

9.34 9.35 9.36 9.37 9.38 9.39 9.40

FIGURE 10.1


FIGURE 10.5

Frequency monitoring errors measured after transmission of 10-Gb/s signals over 320 km of SMF. The inset shows BER curve measured when PM pilot tone was added to optical signal. Experimental setup. Amplitudes and ratio of pilot tones (channel 3) measured using PD 3 and PD 4. (a) Frequency and (b) power errors of seven WDM signals measured after 640-km transmission over SMF. (a) Experimental setup to measure the CD. (b) MI of pilot tones measured at the receiver while varying the CD. Effects of SPM and PMD on pilot-tone-based CD monitoring technique. Monitoring errors caused by (a) SPM and (b) PMD. Operating principle of CD monitoring technique using chirped pilot tones. Experimental setup used for demonstrating CD monitoring technique based on chirped pilot tone. Normalized tone power measured while varying CD. Maximum monitoring error due to PMD measured while varying DGD. Monitoring error due to SPM measured while varying signal power incident on SMF. Experimental setup used for demonstrating CD monitoring technique based on pilot tone carried by BLS. Measured power penalty due to pilot tone. Measured power variation in received pilot tone as a function of CD. Effect of PMD on received power of pilot tones. Experimental setup to demonstrate in WDM system. Measured dispersion and monitoring error of each WDM signal. Magnitude of pilot tone versus tone frequency and DGD. Magnitudes of pilot tones measured while varying both DGD and CD (a) when DSB pilot tone is used, and (b) when SSB pilot tone is used. Electrical noise-free eye prior to ADC at 1700-ps/nm residual CD with symbol ðmÞ patterns di ðm ¼ 3Þ aligned to each interference pattern (a) and according to lookup table obtained at sampling instant t0 = 0 (1 sample/symbol, 4-bit quantization) at OSNR = 14 dB (b). State-based OSNR estimation at a residual CD of 1700 ps/nm and variations in the sampling phase t0. State-based OSNR estimation for NRZ and DB modulation with analog and quantized samples, and for transmission with significant SPM and XPM. Two examples for reference-based estimation of launch power–induced SPM and residual CD for (a) Plp = 0 dBm, CD = 1250 ps/nm, and (b) Plp = 15 dBm, CD = 3050 ps/nm. The black circles indicate the given parameter set, and the white arrow indicates the estimated parameter set. Note: The diagram is rotated in (b). Digital coherent receiver with one optical 90 hybrid for each polarization, ADC, and subsequent digital postprocessing by timing recovery, FIR filter bank, and carrier phase recovery.

List of Figures

FIGURE 10.6 FIGURE 10.7 FIGURE 10.8 FIGURE 10.9 FIGURE 10.10


xxxv

Implementation of FIR filter structure with outer and inner butterfly arrangement. Exemplary coefficients of 11 tap FIR filters for CD = 1000 ps/nm and mean DGD = 30 ps. Effect of PDL depending on input polarization. Copyright # 2009 IEEE. Recovery of the quadratic phase of H^CD(f), for example, of CD = 1000 ps/nm. Copyright # 2009 IEEE. Joint estimation for given CD = 1000 ps/nm and DGD varying from 0, 30 and 60–90 ps over a range of OSNR values between 12 and 24 dB (a) and estimation of pure CD over a wide range (b). The bars indicate the standard deviation of the estimation. Copyright # 2009 IEEE. Deviation mCD and biased error sCD in ps/nm of the mean value of estimated CD compared to given CD for combinations of CD and PDL. Copyright # 2009 IEEE. Estimation example for a CD value of 120,000 ps/nm in a 112-Gb/s PDM-QPSK uncompensated transmission link. The initial scan is performed in steps of dCD = 200 ps/nm. Copyright # 2009 IEEE. Estimation performance versus mean DGD (full-order PMD) for RZ and NRZ pulse shaping of 112-Gb/s PDM-QPSK (OSNR = 14 dB) and PDM-16-QAM (OSNR = 17 dB). Copyright # 2009 IEEE. Exemplary state of polarization evolution in Stokes space. SOP evolution in presence of PDL for 43-Gb/s PDM-QPSK. Copyright # 2009 IEEE. Estimation example for a PMD of 10 ps of the instantaneous mean DGD ht(f)i for CD = 1000 ps/nm and 9-dB worst-case PDL. Note that the distribution of ht(j)i proves to be nearly Maxwellian. Copyright # 2009 IEEE. Statistics of exemplary DGD estimation for CD = 1000 ps/nm and 9-dB worst-case PDL with respect to deviation of the estimation error sDGD, mean value mDGD, and standard deviation within 5-ps DGD windows. Copyright # 2009 IEEE. Deviation mDGD and biased error sDGD in ps of the mean value of estimated DGD compared to given DGD for combinations of CD and PDL. Copyright # 2009 IEEE. Simulative PDL estimation for 43-Gb/s PDM-QPSK. Copyright # 2009 IEEE. Averaged PDL power spectrum adapted to various levels according to the influence of noise power in the system. Copyright # 2009 IEEE. Unbiased estimation error sPDL over individual channel realizations with individual DGD for combinations of CD and PDL at an OSNR of (a) 18 dB and (b) 22 dB. Copyright # 2009 IEEE. PDL estimation performance at OSNR = 22 dB versus the number of concatenated PDL elements with a mean PDL of 5 dB. Copyright # 2009 IEEE. (a) Common nonlinear-threshold-based monitor integrated with 2-R optical regenerator. Monitoring signal is ratio of average power measured at P2 normalized by input power at tap P1. The variable optical attenuator can be used to

xxxvi

List of Figures

FIGURE 11.2

FIGURE 11.3



maintain constant input power. (b) Optical spectrum of 40-Gb/s NRZ-OOK signal generated by XPM in SOA and OSNR monitoring sensitivity obtained by moving output filter to different wavelengths. Copyright # 2005 IEEE. Chromatic dispersion monitoring on 40-Gb/s RZ-DPSK signals using XPM in highly nonlinear fiber. (a) Setup and concept: only clock tones from the RZ pulse train are observed in XPM-induced spectrum of CW probe, but chromatic dispersion distorts the signal-creating power at other wavelengths for nearly background-free monitoring. (b) Monitoring signal variation at 25-GHz offset from probe wavelength for various signal input powers. Copyright # 2008 IEEE. (a) 640-GHz-intensity power spectrum generated using crossphase modulation in fiber. (b) Chromatic dispersion and (c) polarization mode dispersion monitoring signals generated by detecting clock tone in intensity power spectrum of a 320Gb/s signal. Permission from Macmillan Publishers Ltd., Nature Photonics # 2009. Common monitoring configurations on an amplified long-haul link. Comparison of optical spectra of signals using (a) linear photodetector and (b) nonlinear two-photon absorption detector. Nonlinear detection reveals differences between 40-Gb/s NRZ-DPSK, 10-Gb/s RZ-OOK signals, and filtered ASE noise. Experimental dependence of the zero-delay peak value of autocorrelation trace as function of OSNR for accumulated dispersions of 0 and 470 ps/nm. Solid lines are curves to guide the eye. Total average power (ASE noise plus signal) is constant upon filtering with a 0.2-nm optical bandpass filter. (a) Comparison of required OSNR between DPSK and OOK formats. (b) Comparison of nonlinear tolerance between RZ-DPSK and RZ-OOK formats. (a) Power penalty as function of accumulated chromatic dispersion for 42.7-Gb/s RZ-DQPSK signal. (b) Power penalty as function of DGD for 42.7-Gb/s RZDQPSK signal. (a) Comparison of robustness to optical filtering for OOK and RZ-DPSK formats. (b) Sensitivity penalty as function of frequency offset for DPSK and DQPSK format. Phase modulation of the optical signal: (a) generation of DPSK signal using MZM, (b) generation of RZ-DQPSK in parallel configuration, and (c) generation of RZDQPSK in serial configuration. Demodulator and balanced receiver for (a) DPSK and (b) DQPSK. (a) Phase distribution and decision regions of received signals with NPN for nonlinear phase shift of 1 rad (left) and 2 rad (right). (b) Corrected signal distribution for nonlinear phase shift of 1 rad (left) and 2 rad (right). (a) Diagram of CD-independent PMD monitoring technique. (b) Alignment of polarizer with respect to signal in case without and with DGD. (c) Variation of RF spectrum power depending on level of DGD. Partial-bit DI for NRZ signal monitoring. Fraction of bit interferes with following bit, which leads to pulse carving in destructive port. Pulses produce a strong clock tone.

List of Figures xxxvii

FIGURE 12.9 Modification to transmitter for pilot tone monitoring techniques. FIGURE 12.10 Improvement of carrier-to-noise ratio (CNR) in DPSK signal over OOK signal for pilot tone monitoring technique. FIGURE 12.11 Diagram of optical circuit for mixing of optical signal with local oscillator light (90 optical hybrid). FIGURE 12.12 Signal constellation obtained using linear optical sampling: (a) PSK signal modulated with MZM, (b) signal modulated with PM at driving voltage of 2/3vP, and (c) PSK signal with OSNR = 13 dB. FIGURE 12.13 (a) Evaluation of OSNR using linear optical sampling. (b) Evaluation of NPN using linear optical sampling. FIGURE 12.14 (a) Diagram of self-homodyne phasor monitor. (b) Symbol values obtained from monitor. (c) Constellation diagram after rotation through phasor-estimated phase. FIGURE 12.15 Constellation diagram obtained with differential phasor. (a) Phase modulation with driving voltage of VP and 1/2VP. (b) PSK modulation without and with phase mismatch between arms of MZM. FIGURE 12.16 Waveform of RZ-DPSK signal and corresponding asynchronous amplitude histogram. Figure also shows parameters used for monitoring signal impairments. FIGURE 12.17 Waveform and the corresponding AAH for the NRZ-DPSK signals. (a) Parameter for monitoring OSNR. (b) Parameters for monitoring of CD. FIGURE 12.18 (a) Diagram of the monitor based on delay-tap sampling. (b) Acquisition of two samples with relative delay t. (c) Construction of delay-tap plot. FIGURE 12.19 Two-tap plot constructed by sampling the constructive and destructive ports of the demodulator DI. FIGURE 12.20 Diagram of phase-offset monitoring technique based on optical receiver and limiting amplifier. FIGURE 12.21 Analysis of OPM techniques by type of monitored impairment. FIGURE 13.1 Configuration of coherent receiver with balanced detector. FIGURE 13.2 Configuration of phase and polarization-diversity receiver. FIGURE 13.3 (a) Basic single-carrier coherent optical system. (b) Block diagram of digital signal processing. FIGURE 13.4 Block diagram of RF OFDM transmitter/receiver. FIGURE 13.5 Time-domain OFDM signal for one complete OFDM symbol with cyclic prefix. FIGURE 13.6 CO-OFDM system with direct up-/down-conversion. FIGURE 13.7 Simplified block diagram of coherent optical spectrum analyzer. LO, local oscillator; PD, photodetector; TZ, transimpedance amplifier. Copyright # 2002 IEEE. FIGURE 13.8 Comparison of measurement of DFB-LD linewidth by COSA and by diffraction grating-based OSA. Copyright # 2002 IEEE. FIGURE 13.9 Schematic for in-band OSNR and spectrum monitoring based on swept coherent detection. FBC, fiber Bragg grating; PC, polarization controller; VOA, variable optical attenuator. Copyright # 2006 IEEE. FIGURE 13.10 Operating principle of in-band, high-resolution swept coherent detection scheme. Copyright # 2006 IEEE.

xxxviii List of Figures

FIGURE 13.11 Block diagram of CD and PMD monitoring using coherent detection. ADC, analog-to-digital converter; BPF, bandpass filter; ESA, electrical spectrum analyzer; OSA, optical spectrum analyzer. Copyright # 2005 IEEE. FIGURE 13.12 Schematic of coherent frequency–selective polarimeter. BPF, bandpass filter; LPF, low-pass filter; PA, power amplifier; PH, photodetector; PT, polarization transformer. Copyright # 2004 IEEE. FIGURE 13.13 Coherent transmission system with butterfly-structured equalizer filter. Copyright # 2008 IEEE. FIGURE 13.14 Block diagram of real-time monitoring transmission experiment. Tunable laser and polarimeter were used to measure PMD of channel independently of monitor. Copyright # 2008 IEEE. FIGURE 13.15 Schematic of investigated OFDM system. OFDM parameters: data rate 100 Gb/s, 256 subcarriers, 16-QAM, 12.5% CP. Twelve spans: 80 km-SSMF per span, D = 17 ps/nm/km, mean PMD = 10 ps. Copyright # 2009 IEEE. FIGURE 13.16 (a) Estimated DGDs at OSNR = 20 dB using Savitzky-Golay filtering. (b) Estimated inverse SNR versus optical input power. Copyright # 2009 IEEE. FIGURE 13.17 Conceptual diagram of TITO coherent optical MIMO-OFDM model. FIGURE 13.18 Performance of CD monitoring through channel estimation. FIGURE 13.19 Monitored system Q and OSNR as function of input OSNR. FIGURE 13.20 Experimental setup for optical performance monitoring with SITO-MIMO CO-OFDM system. (There is a polarization controller before DGD emulator.) AWG, arbitrary waveform generator; DMZ, dual MZ modulator; EDFA, erbium-doped fiber amplifier; TDS, time-domain sampling scope. FIGURE 13.21 (a) OSNR monitoring result. (b) Q-factor monitoring result. Both are measured in back-to-back transmission. FIGURE 13.22 Estimated channel responses for (a) x and (b) y polarization components X-axes are the frequencies normalized to OFDM subcarrier spacing. FIGURE 13.23 (a) CD monitoring versus transmission distance with and without DGD. (b) CD monitoring error versus DGD after 1008-km transmission. FIGURE 13.24 Monitoring results in CO-OFDM system with 16-QAM for (a) OSNR and (b) Q-factor. FIGURE 13.25 CD monitoring result in CO-OFDM system with 16-QAM. FIGURE 14.1 Business interface model. FIGURE 14.2 Data networking and transport networking. FIGURE 14.3 ASTN architecture. FIGURE 14.4 Modeling of layer networks. FIGURE 14.5 OTN information structure. FIGURE 14.6 Transfer parameters of reconfigurable OADM without amplifiers. FIGURE 14.7 TMN-integrated maintenance process. FIGURE 14.8 OTN performance metrics. FIGURE 14.9 OSNR measurement method. FIGURE 14.10 Noise shaping. (a) Various noise floors caused by OADM. (b) OSNR errors caused by various noise floors.

List of Figures

FIGURE FIGURE FIGURE FIGURE FIGURE

14.11 14.12 14.13 14.14 14.15

FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE

14.16 14.17 14.18 14.19 15.1 15.2 15.3 15.4 15.5


15.6 15.7 15.8 15.9


xxxix

Power spectral density. In-band OSNR measurement with polarization extinction method. Eye diagram and amplitude histograms. Error probabilities. Influence of dispersion on 10-Gb/s binary signal. Without dispersion: (a), (c), and (e); with dispersion: (b), (d), and (f). BIP8 processing. Transmitter (a), Receiver (b). Optical performance monitoring overview. Embedded monitoring and external test equipment. Fault coverage versus cost for monitoring equipment. Typical bidirectional long-haul optical transmission link. Multiple-channel DWDM transmitter/receiver. Amplified link. Raman amplifier. Accumulated dispersion along long-haul transmission system. (a) Dispersion management system. (b) Schematic of evolution of cumulated dispersion along transmission distance. Fiber dispersion. Example of OSA/OCA measurement in DWDM transmitter. OSA/OCA monitoring at receiver node. Required OSNR and receiver sensitivity: achieving error-free operation (BER < 10–12) after 1000-km SMF transmission with no CD compensation using special SCM transmitter/receiver. Intermediate repeater node with and without add-drop channels: (a) add-drop node, and (b) intermediate node without add-drop. Fiber PMD. Example of 40-G transceiver using PMD compensator. Spectrum of long-haul transmission.


List of Tables Table 3.1

Overview of Dispersion Tolerance at 42.7 Gb/s

Table 3.2 Table 4.1

Summary of Selected Advanced CD Monitoring Techniques Summary of Major PMD Monitoring Techniques

Table 5.1 Table 7.1

Comparisons of Monitoring Schemes for Timing Misalignment Summary of Optical Performance Monitoring Techniques Covered in This Chapter

Table 7.2

Independent Validation of Impairment Emulator

Table 7.3

Test Errors for Transponder T2 Showing Effects of Including Combinations of Transponders in Training

Table 7.4

Simultaneous OSNR and CD Measurements on 800-km WDM Test Bed

Demonstrating Improvement in DGD and CD Accuracy with Increasing Size of Training Set Table 10.1 Comparison of Measured and Estimated OSNR from Measured Data for Various Combinations of Residual CD and Launch Power Table 7.5

Table 10.2 Normalization Constants Table 11.1 Nonlinear Effects and Their Applications to OPM Table 12.1 Comparison of Measurement Ranges between OPM Techniques for Phase-Modulated Signals Table 13.1 OFDM Parameters for 4-QAM Transmission Table 13.2 OFDM Parameters for 16-QAM Transmission Table 14.1 G.805 Architectural Components Table 14.2 OTN Services Table 14.3 OTN Supervision Table 14.4 Comparison of OTN Monitoring Methods Table 15.1 Optical Signal Performance under Normal Conditions Table 15.2 Optical Signal Performance for EDFA under Normal Conditions Table 15.3 Typical OSNR Requirements for Long-Haul System Table 15.4 Example of 10G System Budget Allocation (NRZ, 2000 km, SMF) Table 15.5 Chromatic Dispersion and Polarization Mode Dispersion Requirements for Typical Direct Detection System

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Preface These days, optical networks worldwide have been widely deployed in various network scales, including long-haul backbone and metropolitan areas, as well as regional broadband access. In order to assure a certain quality of service and a service-level agreement of the data delivery as requested by the network service subscribers, network management plays a very important role in the operation and administration of practical optical networks. Performance management is among the key aspects of network management that assures the signal quality during signal transmission, switching, and routing. It provides several important network functions including (1) providing feedback in the adaptive signal compensators and equalizers; (2) control of network elements; (3) link setup, control, and optimization; and (4) fault forecasting, detection, diagnosis, and localization, as well as resilience mechanism activation. Conventionally, performance management in optical systems and networks involves simple measurements of system parameters such as optical power, wavelength values, and the system noise levels, followed by the correlation analysis using the network management software. With the ever-increasing network bandwidth demand, as well as the advances in optical technologies, the signal transmission data rate on each wavelength channel has been increasing beyond 40 Gb/s or even more. At the same time, the wavelength-division multiplexing technique has also been widely adopted to combine multiple wavelength channels on the same fiber link, thus it has greatly enhanced the system transmission capacity. However, such high-speed optical signals are more vulnerable to some optical system impairments such as fiber chromatic dispersion, polarization mode dispersion, fiber nonlinearity, etc., and the induced performance degradations are getting more prominent. Therefore, more advanced optical signal processing techniques are required to characterize the signal quality and assure the transmission performance of such high-speed optical signals in various kinds of modulation formats. Over the past decade there have been numerous research efforts in devising many advanced techniques for optical performance monitoring (OPM) for optical systems and networks. Most of the reported OPM techniques have been scattered throughout journals, conference proceedings, and several book chapters. Yet, there is no such book available in the market solely for the discussion of OPM techniques. This book aims to provide comprehensive and in-depth discussions of these advanced OPM techniques, which would be expected to play an important role to facilitate the network management of the next-generation photonic networks. All chapters in this book have been prepared and written by the leading experts and researchers in the OPM field. The first chapter provides a comprehensive overview of all the relevant aspects of OPM, as well as its perspectives and challenges. The following fourteen chapters are organized in three different sections. The first section comprises Chapters 2–5, which discuss the OPM techniques for several common performance metrics, including optical signal-to-noise ratio, chromatic dispersion, polarization mode dispersion, and timing alignment, respectively. Each chapter provides an extensive overview and discussion of various feasible approaches to characterize the respective performance metric. These approaches will be further compared in terms of their technical merits and limitations, as well as their robustness in the presence of other system impairments. The second section comprises Chapters 6–11, which discuss several selected advanced OPM techniques, based on asynchronous amplitude histograms, asynchronous delay-tap sampling, linear

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Preface

optical sampling, radio-frequency pilot tones, electronic digital signal processing techniques, and nonlinear optical effects, respectively. The goal of these chapters is to provide a detailed and indepth discussion of the principles of the advanced optical signal processing techniques that could be applied for OPM. Although some of these OPM techniques may have also been included as the feasible approaches in the chapters in the first section, the focus and treatment of this discussion will be much different. In the first section, the principles of the feasible approaches for monitoring the individual performance metric may not be discussed in detail, as the variety of techniques is the focus. In contrast, in the second section, the principles and theories of those OPM techniques are discussed in detail, and OPM is just their application. I believe this treatment of discussion in both breadth and depth will give the reader a more thorough understanding of both the principles and the applications of the advanced OPM techniques discussed in this book. The third section comprises Chapters 12–15, which discuss the applications and roles of OPM in various optical systems and networks, including phase-modulated optical systems, coherent optical systems, optical transport networks, and optical long-haul transmission systems, respectively. In view of the different system requirements and unique characteristics of these optical systems and networks, the considerations of OPM and the respective techniques will be quite different. On the whole, this book is intended as a general reference for researchers in both academia and the industry who work in the field of optical networks. It covers most of the recent advances of the optical signal processing techniques for OPM in various optical systems and networks. The comprehensive discussions have been carefully organized to illustrate the principles and applications of OPM in both breadth and depth. Calvin C. K. Chan Hong Kong, 2010

Acknowledgments First, I would like to express my gratitude to all chapter contributors who have kindly spent their precious time and made tremendous efforts to contribute to this book. All of the chapter contributors are really the leading experts in this area of optical performance monitoring. I believe their collective efforts will definitely make this book a great success. I would like to thank Prof. Lian-kuan Chen, Prof. Kwok-wai Cheung, Prof. Frank Tong, and Prof. Chinlon Lin at the Chinese University of Hong Kong for their enlightenment, encouragement, and collaboration in this area of optical networks and system technologies. I would also like to thank Elsevier for giving me the opportunity to prepare this book and the valuable support and excellent project management from its staff, especially Tim Pitts, Melanie Benson, Naomi Robertson, and Sarah Binns. Their full support has made a smooth preparation of this book. Last, but not the least, I am most indebted to my wife Esther, daughter Ariel, and son Leo, for their consistent love, encouragement, and patience.

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List of Contributors Trevor B. Anderson Monitoring Division Inc., Melbourne, Australia; and National ICT Australia Ltd. (NICTA), Victoria Research Laboratory, Australia Paulo Andre´ Instituto de Telecomunicaço˜es and Departamento de Fı´sica, Campus de Santiago, Aveiro, Portugal Lian-Kuan Chen The Chinese University of Hong Kong, Hong Kong SAR, The People’s Republic of China Yun C. Chung Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Korea Ken Clarke Monitoring Division Inc., Melbourne, Australia Liliana Costa Instituto de Telecomunicaço˜es, Campus de Santiago, Aveiro, Portugal Sarah D. Dods Monitoring Division Inc., Melbourne, Australia Christophe Dorrer Laboratory for Laser Energetics, University of Rochester, Rochester, New York Wolfgang Grupp JDSU Deutschland GmbH, Eningen u.A., Germany Fabian N. Hauske Huawei Technologies, European Research Center, Munich, Germany Don Hewitt National ICT Australia Ltd. (NICTA), Victoria Research Laboratory, Australia Xin Jiang Department of Engineering Science and Physics, College of Staten Island; and The City University of New York, Staten Island Daniel C. Kilper Bell Laboratories, Alcatel-Lucent, New Jersey Adam Kowalczyk Monitoring Division Inc., Melbourne, Australia; and National ICT Australia Ltd. (NICTA), Victoria Research Laboratory, Australia Bartłomiej Kozicki NTT Network Innovation Laboratories, NTT Corporation, Japan

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List of Contributors

Maxim Kuschnerov University of the Federal Armed Forces, Munich, Germany; and Institute for Communications Engineering, Neubiberg, Germany Jun Haeng Lee Samsung Advanced Institute of Technology, Samsung Electronics, Korea Jonathan C. Li Monitoring Division Inc., Melbourne, Australia Ruben S. Luis Instituto de Telecomunicaço˜es, Campus de Santiago, Aveiro, Portugal; and Center of Volcanology and Geological Risk Assessment, University of Azores, Ponta Delgada, Portugal Zhongqi Pan Department of Electrical and Computer Engineering, University of Louisiana at Lafayette Paul K.J. Park Samsung Electronics, Korea William Shieh Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, Australia Yan Tang Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, Australia Antońio Teixeira Instituto de Telecomunicaço˜es and Departamento de Electrońica, Telecomunicaço˜es e Informatica, Campus de Santiago, Aveiro, Portugal Alan Willner Department of Electrical Engineering, University of Southern California, Los Angeles Xiaoxia Wu Department of Electrical Engineering, University of Southern California, Los Angeles Jeng-Yuan Yang Department of Electrical Engineering, University of Southern California, Los Angeles Xingwen Yi Department of Electrical and Computer Engineering, University of California, Davis Changyuan Yu National University of Singapore; and A*STAR Institute for Infocomm Research, Singapore

About the Editor Calvin C. K. Chan received his B.Eng., M.Phil., and Ph.D. degrees from the Chinese University of Hong Kong, all in Information Engineering. In September 1997, he joined the Department of Electronic Engineering at the City University of Hong Kong as a Research Assistant Professor. At both universities he worked on design and experimental demonstration of high-speed all-optical timedivision-multiplexed and wavelength-division-multiplexed tunable channel multi-access networks. He also worked on surveillance techniques for fault identification in various kinds of optical network elements. In June 1999, he joined Bell Laboratories, Lucent Technologies in Holmdel, NJ, United States, as a member of the technical staff, where he worked on control of widely tunable semiconductor lasers and realization of an optical packet-switch fabric with terabit-per-second capacity. In 2001, he served as Senior Optical System Engineer at Jedai Broadband Networks, Inc. in New Jersey, where he worked on the design of optical access networks and optical wireless systems. In August 2001, he joined the Department of Information Engineering at the Chinese University of Hong Kong, and now serves as an Associate Professor there. He has served as a member of the Technical Program Committees on many international conferences, including the prestigious OFC/NFOEC, OECC, ICCCAS, Photonic in Switching, APOC, ICOCN, COIN, ChinaCom, ICAIT, etc. Currently, he serves as an Associate Editor for IEEE/OSA Journal of Optical Communications and Networking. Dr. Chan has published more than 180 technical papers in refereed international journals and conferences, as well as two book chapters. He holds one issued U.S. patent. His main research interests include optical metro/access network architectures and their enabling technologies, high-speed optical packet-switching techniques, and optical network management.

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CHAPTER

Optical performance monitoring: Perspectives and challenges

1

Alan Willner, Xiaoxia Wu, Jeng-Yuan Yang Department of Electrical Engineering, University of Southern California, Los Angeles, California, USA

1.1 INTRODUCTION Today’s telecommunication networks function in a fairly static fashion and are built to operate within well-defined specifications. A single parameter out of specification can bring down the entire optical network. Since the parameters can change over time, operating and managing an existing network requires a fair amount of labor resources. As the demand for increasing bandwidth and higher data rates creates increasingly complex networks, more variables are introduced that decrease the mean-time-to-failure and increase the mean-time-to-repair if a failure occurs. The forthcoming IEEE 100 Gb/s standard, 802.3ba, provides greater bandwidth, higher data rates, and a mixture of advanced modulation formats, promising to tighten the window of network operability and further, as illustrated in Figure 1.1, emphasizing the need for a “smart” network capable of managing its operation as parameters vary.1,2 Optical performance monitoring (OPM) is one potential means of either widening this window or helping to maintain channel operation within a small window, such that rapid growth of high-performance optical networks can be enabled.

Format Number of channels

Bit rate

Power

Nonlinearities Polarization effects

Dispersion

FIGURE 1.1 Window of operability is shrinking as networks become more complex. © 2010 Elsevier Inc. All rights reserved. Doi: 10.1016/B978-0-12-374950-5.00001-8

1

2

CHAPTER 1 Optical performance monitoring

Highly desirable features of a future “smart” network include higher robustness, reconfigurability, flexibility, and security. To enable robust and smart operation, the network should be able to: (1) measure its physical state and the quality of the propagating data signals; (2) automatically diagnose and repair the failures; (3) take actions before data loss and failure occur; (4) allocate resources, including signal wavelength/power, tunable compensation/equalization, data coding, path determination, and channel bandwidth; (5) change routing tables and redirect traffic based on physicallayer conditions; and (6) detect accidental and malicious security risks. All of these desirable features require the ability to first measure the network elements, paths, and data in an accurate and repeatable fashion. This deficiency in the measurement science and metrology of telecommunication networks leads to the overbuilding and inefficient use of current networks. The ability to measure the network infrastructure and resources in a real-time fashion is typically known as network monitoring. In order to enable robust, self-managed, and smart operation, OPM should be able to accurately measure and rapidly reflect the physical states of network elements, paths, and the quality of propagating data signals. The ability to measure both the data quality and the network path’s physical state in a real-time fashion has traditionally proven difficult due to the large number of impairments, the variety of data formats, the variety of data rates, and the continuous growth and change of the network structure. As a result, multiple impairments that cause the alarms must be isolated, localized, and compensated, requiring real-time monitoring and dynamic feedback control. This chapter will first provide an overarching vision and critical challenges of OPM, and then describe the specific parameters that a network might want to monitor, laudable OPM-enabled functionalities for next-generation optical networks, and desirable features of OPM.

1.1.1 Overarching vision The growth of Internet-driven wired traffic has been increasing exponentially, well beyond the simple addition of new users. Fueled by the ability to traverse appreciable distances of fiber optics, optical communication networks will undoubtedly continue to grow, and this growth will be symbiotic in that growth in one area depends on the available capacity in others. Traditionally, data are passed from the wireless or radio-frequency (RF) domain to the fiber optic backbone for long-distance transmission. “Nortel estimates that by 2010 there will be 10 devices connected to the network for every person using them, resulting in five billion connection points.”3 This puts an enormous strain on network resources as users contend for increasing bandwidth and better quality of service. Today, networks suffer from an inability to assess both the physical state of the network and the quality of the propagating data, leading to the over design and inefficient use of network resources. In order to enable a robust, scalable, self-managed, and smart operation, the network should be able to accurately monitor the physical state of network elements, paths, and the quality of propagating data signals. It is becoming clear that OPM will be playing an increasingly important role in managing the emerging networks. One of the major operational areas that carriers probably need to deal with is very quickly identifying a fault that has occurred, where it occurred, and what caused it. As networks move to all-optical technologies, identifying these faults will become more difficult. This is because many of the faults are what we call “soft faults,” caused by things like optical impairments, which do not bring the whole system down, but rather result in noise like conditions that impair communications. OPM will be an essential tool to identify these soft faults and will probably play an important role in reducing the operation costs for the future optical networks.4 Figure 1.2 illustrates a future self-managed network with OPMs integrated.

1.1 Introduction

• • • •

Network controller

Network elements

O P M

Re-route traffic Network control Network management Provisioning

Network elements

Network elements OPM

3

O P M

• Heterogeneous traffic • Variable rates and formats • Various OSNR/CD/PMD

FIGURE 1.2 A self-managed network with optical performance monitoring.

To operate at higher capacity, performance monitors can keep the system above the “red line” and operate under tougher conditions. Network carriers will not need to overbuild (i.e., waste money) the network. Some basic issues are already being tracked in many networks. For example, optical networks look for sudden power loss that would follow a fiber break or a laser/amplifier outage. Moreover, given the widespread adoption of wavelength-division multiplexing (WDM) for transmission and routing, many networks monitor any deleterious wavelength drifts of the channels. Furthermore, it is helpful when existing networks measure the optical signal-to-noise ratio (OSNR) so that the optical amplifier–generated gain and noise can be controlled; note that many receivers are indeed limited by the accumulated amplified spontaneous emission (ASE) noise. All the above parameters can be monitored, to the first order, using fairly straightforward low-speed equipment, such as reflectometers, power meters, spectrometers, and filters. Some companies have been formed to provide this binary level of monitoring, yet none of these methods can provide a holistic system approach capable of simultaneously measuring impairments, locating their source, and providing accurate information that network controllers can use to compensate and re-route.

1.1.2 Challenges As data rates increase and network architectures become more complex, it becomes more difficult to predict and manage data impairments due to the fact that the degradations can change with time. In order to enable a robust and cost-effective “self-managed” operation, the optical network should be able to agilely monitor the physical state of the network and the quality of propagating data signals, automatically diagnose and repair the network, redirect the traffic, and dynamically allocate resources, as illustrated in Figure 1.3. Unfortunately, high-data-rate optical networks are quite susceptible to “noncatastrophic” signaldegrading problems due to channel propagation, in which there is sufficient signal power, but the data bits themselves are unrecoverable due to various fiber dispersive and nonlinear effects. This scenario is a result of the more complex nature of present and future transmission systems, especially when parallel wavelength channels are propagating simultaneously along the fiber. For example,

4


Locate faults

Ubiquitous monitoring Detect attacks

Diagnose and assess Repair damage

Reroute and balance traffic

Malicious behavior

Telcos: Human error (~1/3 of outages)

FIGURE 1.3 Features of ubiquitous monitoring for robust and self-managed networks.

the in-band OSNR may vary among channels, and when coupled with repeated filtering and amplification, direct measurement can be very difficult. Additionally, impairments can interact with each other to degrade the data signal. The effort of research work is to develop the efficient OPM techniques and to overcome the following significant technical challenges.5–10 1. Monitor and isolate all degrading effects: Provide a real-time monitoring scheme that efficiently predicts all degrading effects on the high-speed data with a minimum of optical hardware. 2. Localize any degradation: Localize any degrading effects for network repair and traffic re-routing without requiring monitors at infinitely small distances. 3. Fast response time: The output of the optical monitor must be interpreted to provide meaningful information on the physical state of the network. The output is a simple number processed from the summary data, which can be used by the network controller to efficiently monitor the specific link or data channel. 4. Take action to dynamically allocate resources: Interpret the output of the network controller in order to change the channel wavelength, optical and/or electrical dispersion compensator, data modulation format, bit rate, gain of an amplifier, and the allocated channel bandwidth. 5. Prevent denial-of-service and eavesdropping: Keep track of any and all wavelengths and determine whether any nonlinear products are being generated. Protect the system in the secure condition by notching out any unwanted wavelengths.

1.2 PHYSICAL-LAYER MEASUREMENTS AND ROUTING DECISIONS IN TODAY’S OPTICAL NETWORKS The development of optical amplifiers and WDM has allowed systems to carry more traffic over longer distances, but this makes performance monitoring more challenging. At present, monitoring of performance in the physical layer primarily involves a combination of individual component alarms,

1.3 Signal parameters requiring monitoring and OPM techniques

5

aggregate power, and in some cases, optical channel monitoring (OCM).11 Component alarms include monitoring of parameters such as amplifier pump laser power or temperature controller limits. In this case, OPM is indirectly realized through the assumption that if all of the components are working correctly, then the signal must be good. This is a very powerful technique that largely comes for free, but unfortunately there are signal failures that violate this assumption. OCM includes measurements of channel power, presence, and wavelength. From an OPM perspective, channel monitoring as well as aggregate power monitoring are extensions of component alarms in that they indirectly measure signal quality. The term “OPM” is often applied to OCM devices with the additional capability of monitoring the OSNR, as well as other signal quality measures, such as chromatic dispersion (CD) and polarization mode dispersion (PMD), or jitter. Band-channel monitors that are sensitive to the per-channel signal-to-noise ratio (SNR) are referred to as “signal quality monitors.” Techniques such as Q-factor monitoring are perhaps the closest optical analog of the electronic performance monitor. As optical communication systems have become more sophisticated, the need has arisen for more sophisticated performance monitoring. Fortunately, many technologies that enable improved system reach and capacity can simultaneously be used to enhance system performance monitoring, which will be discussed in subsequent chapters of this book. Note that in these cases, the cost of the performance monitors can be fully justified on the basis of the system’s improved performance. Not all monitors are so easily justified, but service providers have a history of deploying additional equipment when necessary. There is no level of performance monitoring that is superfluous; there are only levels that are too expensive.8

1.3 SIGNAL PARAMETERS REQUIRING MONITORING AND OPM TECHNIQUES 1.3.1 Optical impairments

The specific parameters that a network might want to monitor range from the most basic to the highly complex. Some issues are already being monitored in many networks. For example, optical networks monitor the abrupt power loss that would follow a fiber break or a laser/amplifier outage. Moreover, given the widespread adoption of WDM for transmission and routing, many networks monitor any deleterious wavelength drifts of the channels. When the existing networks monitor the OSNR, simultaneously controlling the optical amplifier–generated gain and noise is helpful; note that many receivers are indeed limited by the accumulated ASE noise. High-data-rate optical networks are quite susceptible to various “noncatastrophic” signal degrading problems due to fiber transport, in which there is sufficient optical signal power but the data bits themselves are unrecoverable. When parallel wavelength channels are propagating simultaneously along the fiber, this scenario becomes more complex. Although the signal quality and bit error rate can be determined after a high-speed photodetector, such signal recovery cannot readily provide the location and cause of a problem; the signal’s phase information will be lost using common direct detection and the origin of the problem might remain a mystery. The root causes cannot be specified in advance because they include all of the various modes of component failures, but it might be helpful to further divide impairments into either component fault effects or optical transmission impairments.

6


Component faults include individual or multiple component malfunctions, improperly installed or configured equipment, and damage or intrusion to the network. Impairments due to such faults are as diverse as the components and network designs deployed in the field and cannot be comprehensively cataloged. Kartalopoulos cataloged the more common component failure modes in DWDM systems.12 To appreciate the range of possibilities, we consider for example optical amplifier failure modes. Failing pump lasers will result in reduced power at particular points in the transmission path or within internal sections of the amplifier. Subsequent amplification can convert these low power levels into excess noise that will show up in the optical spectrum. Unstable pump lasers can also result in excess noise on the signal, but in this case it may not show up in the optical spectrum. If the power control loop on a pump laser fails and it runs high, then the signal levels will be high, which can enhance a host of nonlinear effects in transmission, such as self-phase modulation (SPM) or cross-phase modulation. Optical transmission impairments, which can also be referred to as fiber degradations, will cause various signal distortions and thus result in diverse information (e.g., eye diagrams, optical and electrical power variations, and polarization fluctuations) for monitoring. For example, PMD results from the birefringence nature of the fiber core, which is different from the CD effect. However, it is difficult to monitor and isolate PMD from CD since both of them cause pulse distortions and change the monitored information. They might result in a similar variation (e.g., RF clock-tone power fading), making the monitored information ambiguous. Moreover, each of the optical impairments gives rise to different degrading effects for different data modulation formats. Note that the future challenge is to develop a simple and unique monitoring technique that can monitor each of the optical impairments simultaneously for any input data formats. Essential fiber-based degrading effects due to transmission include the following: •

•

Chromatic dispersion. Because the refractive index of fiber is slightly dependent on frequency, different frequencies, and thus different channels, propagate at different speeds. Data modulation causes monochromatic laser light to spread out in frequency roughly on the same order as the data modulation. This causes each bit to spread in time. The unit of chromatic dispersion is (ps/nm)/km; thus, shorter time pulses, wider frequency spread due to data modulation, and longer fiber lengths will each contribute to temporal dispersion. The CD tolerance is proportional to the inverse of the square of the bit rate and thus 40-Gb/s signals are 16 times more susceptible to CD than 10-Gb/s signals. Due to the reduced tolerance to CD for the high-data-rate signals (i.e., >40 Gb/s), the accuracy of monitoring becomes increasingly crucial. CD also changes slightly with temperature, and there is some uncertainty in any deployed fiber plant. Polarization mode dispersion and polarization-dependent loss. The core of an optical fiber is not perfectly circular, and the resultant ellipse has two orthogonal axes. The “effective” refractive index of a waveguide, which determines the speed of light, depends on the glass material itself as well as the shape of the waveguide relative to the traveling wave. Therefore, light polarized along one axis travels at a different speed than does the light polarized along the orthogonal axis. PMD has emerged as a key issue for deploying high-speed optical communication systems. Differential group delay (DGD), which is known as the first-order PMD, causes walk-off in time between the two orthogonal polarization states. Moreover, the PMD effects on the data signal are stochastic, time varying, and a random process that acts on each WDM channel differently. A system can operate flawlessly for 364 days and then break down temporarily on the 365th day. Any adaptive PMD compensator must require accurate PMD monitoring in order to

1.3 Signal parameters requiring monitoring and OPM techniques

•

•

•

7

dynamically track the degrading effects due to time-varying conditions. Furthermore, the two polarization axes exhibit a different loss, the so-called polarization-dependent loss (PDL). PMD and PDL can interact to produce higher-order distortions.13 PMD and PDL are two of the key limitations in systems that operate at greater than 10 Gb/s, especially in high-speed polarization multiplexed systems. OSNR degradation. One of the major parameters to monitor is OSNR degradation since it provides direct and important information about the channel quality inside a dynamic network. Typically, OSNR can be measured by the linear interpolation between the channels using a tunable narrowband filter and an optical spectrum analyzer (OSA). However, in reconfigurable optical networks, WDM channels are added, dropped, or cross-connected in the optical layer. One channel may travel through different paths, different numbers of optical amplifiers, and cascaded filtering effects, resulting in a nonuniform noise spectrum compared with adjacent channels. The accumulated noise level may be quite different from channel to channel. As a result, the accurate OSNR of each channel must be monitored in-band. Fiber nonlinearities. The refractive index of fiber is slightly dependent on optical power, with higher intensities experiencing a higher index. Moreover, the electric fields of several channels will mix with each other and produce sum and difference frequency products. Nonlinearities can be controlled by carefully introducing and balancing chromatic dispersion, perhaps with fixed and/or tunable compensation. Some typical nonlinear effects, which tend to degrade significantly the signal integrity, include SPM (the intensity profile of an optical pulse on a single channel causes an index-of-refraction profile and, thus, the higher intensity center of a pulse travels slower than the lower-intensity pulse wings); XPM (when considering many WDM channels, power from channel 2 through channel N can distort the index profile that is experienced by channel 1, which can be translated into a lightwave speed distortion); and FWM (the optical intensity propagating through the fiber is the square of the electric field and when one squares the sum of different channels’ fields, products emerge at various sum and difference frequencies). If a WDM channel exists at one of the FWM beat-term frequencies, then the beat term will interfere with this other channel and potentially distort the data. Other nonlinear effects include stimulated Rayleigh scattering (SRS), stimulated Brillouin scattering (SBS), and so on. Frequency chirp. Frequency variation across the time period of a bit is defined as the frequency chirp, which results in a phase-modulation, parasitic-causing frequency broadening of the data. For high-speed and long-haul transmission systems, chirp interacts with CD to further distort the data. Besides directly modulated lasers and optical modulators, even passive components such as an optical filter may also induce chirp. Each component normally results in a different type of chirp, making it very challenging to be monitored, distinguished, and compensated.

Figure 1.4 gives an overview of various optical impairments within the network, which also includes some other notable transmission impairments, such as: • • • • • •

Amplifier noise Amplifier distortion and transients Timing jitter Interference effects (MPI) Optical filter distortion Linear crosstalk

8


• Power loss • Wavelength drift • Chirp

• Loss, CD, PMD • SPM, XPM, FWM • MPI Transmitter

Fiber

Amplifier

Receiver • Noise • Bandwidth • Sensitivity

Network element

• ASE noise • Gain fluctuation • Power saturation

• Wavelength alignment • Power condition • Linear crosstalk

FIGURE 1.4 Overview of various optical impairments within the network.

1.3.2 OPM techniques Advances in measurement science and metrology enable a more accurate and flexible characterization of optical signals. Increasing the transmission capacity can be attained by utilizing the amplitude, phase, and polarization of optical waves to send information at ever-increasing data rates. At the higher transmission rates, even a weak impairment in the link could severely distort some of the optical wave properties that impact information capacity. Comprehensive OPM will be required to manage the communication network with enhanced security and full utilization. A comprehensive approach also means developing a set of measurements and the corresponding methods and tools through metrology designed to meet the demanding requirements of highly utilized networks. For example, in data-intensive networks, we require a technology to monitor transmission degrading effects simultaneously. On the other hand, in local clients, cost-effective and repeatable measurements are required without the need for a short response time. OPM can be performed by measuring changes to the data and determining “real-time” changes resulting from various impairments, such that a change in a particular effect will change a measured parameter. This can employ (1) electrical postprocessing techniques in the cases of either non-coherent detection or coherent detection,14–16 which are referred to as time-domain OPM; or (2) optical techniques to monitor changes in a RF tone power or in the spectral channel power distribution,17 which are referred to as frequency-domain OPM. •

Time-domain OPM. Besides the specific case of using coherent detection for OPM,14–16 several techniques have been proposed for OPM using offline digital signal processing of received electrical data signals.18–31 Five of these methods18–22 utilize amplitude histograms, power distributions, or asynchronous sampling to monitor the optical performance in various forms, such as bit error rate (BER); four23–26 employ delay-tap plots to distinguish among impairments; three27– 29 use pattern recognition techniques to identify multiple impairments; and the rest30,31 use parameters derived from eye diagrams, histograms, and delay-tap plots for the same purpose.

1.4 Laudable OPM-enabled functionalities in next-generation optical networks

•

9

In addition, linear optical sampling (LOS) has been applied to recover the phase and amplitude of a high-speed advanced data modulation format simultaneously.32–38 LOS requires a low-speed pulsed-laser limited only by the pulsewidth, and thus LOS is very applicable to the monitoring of high-speed advanced modulation formats. These techniques will be reviewed in great detail in Chapters 2–11. Frequency-domain OPM. The frequency-domain measurement approaches are proposed for OPM, which are cost-effective and simply implemented. Although in some cases the frequency domain approaches tend to monitor a single impairment at one time, several frequency-domain techniques can potentially form an OPM toolbox, and in particular meet the technical requirements imposed in achieving a smart assignment and management of network resources.39–45 Similar frequency-domain techniques have also been applied to monitor the time misalignments in the transmitter with advance phase-modulated formats.46,47 Repeatability and stability enabled by the frequency domain measurement approach are highly desirable in future “smart” networks. Detailed discussions about the frequency-domain OPM techniques will be given in subsequent chapters.

Driven by the need for spectral efficiency, the high-speed, phase-modulated data channel is becoming increasingly important for 100-Gb/s Ethernet and higher-capacity optical networks.48 In order to further advance the metrology and accuracy of measurements for robust and reconfigurable high-capacity optical networks, developing more cost-effective, efficient, and stable OPM techniques becomes increasingly crucial.2,49,50 Chapters 12–15 will review the use of OPM in various transmission systems, including high-speed, optical-phase modulation formats, synchronous digital hierarchy (SDH), and synchronous optical network (SONET).

1.4 LAUDABLE OPM-ENABLED FUNCTIONALITIES IN NEXT-GENERATION OPTICAL NETWORKS OPM plays an important role for maintenance and management of high-speed, intelligent, and reconfigurable optical networks. In this section, we give a brief introduction to the network functionalities that can be enabled by using OPM.

1.4.1 Robust and stable operation Multiple impairments on optical signals are time varying due to a changing environment, drift of components, and rapid reconfiguration of network paths. Moreover, the fiber impairments depend on complex interactions of linear and nonlinear fiber effects, which in turn are a function of the signal power, data rate, and data modulation format. These properties imply that the OPM should provide real-time information about the quality of these transmitted signals and also isolate the specific cause and location of the problem. To enable a robust and stable operation, the monitoring information corresponding to the accumulated impairment due to each specific degrading effect should probably be deployed ubiquitously around the network. Furthermore, it can be quite advantageous to determine when a data signal is beginning to degrade, so that the network can take action to correct the problem (i.e., change a laser wavelength, tune a compensator) or to route the traffic around the degraded area.51

10


1.4.2 Accommodate transparency High-speed (i.e., >40 Gb/s) differential phase-shift-keying (DPSK) data format has generated much excitement due to its high receiver sensitivity and tolerance to nonlinearities as compared to the conventional on-off-keying (OOK) format.48 For high-capacity transmission systems, spectrally efficient data modulation formats are highly desirable due to their ability to pack more information into narrow wavelength channels, enhance the tolerance to fiber dispersion, and remove the necessity for high-speed components. The use of multilevel PSK formats, such as differential quadrature phaseshift-keying (DQPSK), offers high spectral efficiency and tolerance to fiber-dispersive effects and leverages the limitations on the available operation bandwidth of the RF/optical modules. Furthermore, fitting more bits/s/Hz on any channel will require the utilization of polarization multiplexing two independent data streams on the two orthogonal polarization states to reduce the received power, required hardware, and network management cost.52 As a consequence, the high-capacity transmission systems tend to polarization multiplex two DPSK or DQPSK channels for better receiver sensitivity and robustness to dispersion and nonlinearities.53 It would be highly desirable for the performance monitor to be independent of the input data format and to accommodate a convergence of different traffic types, including amplitude modulation, phase modulation, and polarization multiplexing for binary and multilevel coding (i.e., OOK, PSK, and QPSK). Particularly for an optical high-capacity network, it seems inefficient to build a separate optical network/monitor to accommodate each one. Therefore, the performance monitor should be able to effectively handle a wide variety of high-rate traffic. Since each application might have a different set of optimal requirements, one can envision that one network might be required to transmit different modulation formats, a wide range in data rates, commercial and military traffic, and variable quality of service (QoS), as shown in Figure 1.5. Therefore, OPM should accommodate transparency.14,51

1.4.3 Impairment-aware routing In the existing networks, traffic routing is typically based either on a shortest-path calculation or on paths that satisfy certain network QoS constraints (e.g., data rate, delay, jitter, or packet loss). However,

Different modulation formats

Variable QoS

Variable bit rate

Future heterogeneous network

Multiple wavelength ranges

Subcarrier multiplexing (D + A)? Circuit + packet switching?

FIGURE 1.5 Future heterogeneous networks should accommodate various types of traffic and use optimal channel characteristics for each application/user. The required hardware should be reconfigurable and transparent.

1.4 Laudable OPM-enabled functionalities in next-generation optical networks

11

it might be quite advantageous for the network controller to also take into account the variable physical state of the network, especially given that transparency domains are growing, data rates are increasing, and channel spacing is denser. It might be valuable for the network controller and routing algorithms to consider the optical-layer impairments that degrade the BER of the signal.54 Performance monitoring provides valuable information such that routing tables themselves can dynamically reflect the state of the physical links to the network controller. For example, each link has a set of parameters (a, b, c) and each node has a set of parameters (a, b), as illustrated in Figure 1.6. The ranges of these parameters will be determined. Impairment-aware routing enables routing tables to be modified based on the physical changes of the links so that the network controller can agilely control and manage the heterogeneous networks. The network must interpret the “cost function” for the routing tables and determine ranges of these parameters for inclusion into the network model. Therefore, OPM might be required to provide valuable information such that routing tables themselves can dynamically reflect the state of the physical network links, the fidelity of the channels, and the addition/deletion of nodes. This “impairment-aware” routing would enable routing tables to be modified based on physical changes in the links, instead of routing decisions being based simply on of the fewest hops and shortest links between source and destination. This results in severe challenges in terms of algorithm complexity and interpreting monitor output for any protocol. Network links today are generally assigned either a “1” or “0” depending on whether the link is considered working or malfunctioning. However, a link might function well for some traffic but not sufficiently well for all traffic. Therefore, we emphasize that the network could assign “weights” to each link, such that a partially functioning link can still be used for some traffic, while re-routing other traffic. This is analogous to closing down only two lanes of a four-lane highway, which is significantly more preferable for traffic management than shutting down the entire highway. The backbone network will likely be a transparent WDM system. The key benefits of all-optical transport networks are the transparency to bit rate, protocol, and modulation format of all the various wavelength channels propagating in the system. However, key challenges exist when determining an

a. Fiber length b. Signal degradation c. Amplification and transients , c i> 1-bit duration’s worth of DGD. Due to the pseudo-random nature of the data, the minimum DOP is 0.5. Figure 4.16 shows theoretical results of minimum DOP versus DGD (relative to the bit time, Tb) as the pulse width of an RZ signal varies.

4.3 PMD monitoring based on measurement of degree of polarization

1

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0.8

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113

MZ(a=0)

0.6

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a > 0; 0 ps/nm a > 0; +350 ps/nm a > 0; –350 ps/nm a < 0; 0 ps/nm a = 0; 0 ps/nm

0.2

MZ(a>0,a40 Gb/s), EAM is very attractive for data modulation. In Reference 3, a device to detect and correct the timing misalignment between the pulse carver and the EAM for data modulation in a 10-Gb/s ultra-long-haul soliton transmission system is demonstrated. The scheme utilizes the chirping characteristics of the EAM for the timing misalignment monitoring. When timing misalignment occurs, the RF power of the upper sideband and lower sideband of the signal spectrum will increase or decrease. As shown in the inset of Figure 5.2, the solid (dashed) curve is the optical spectrum when pulse carver leads (lags) the data modulator by 20 ps. By taking the power difference between the 10-GHz and 10-GHz sideband, the timing offset can be inferred, as shown in Figure 5.2. The two sidebands are extracted using a wedge etalon. The timing misalignment and the power difference depict a linear relation within 20 ps of misalignment, showing that it is a feasible scheme for misalignment monitoring using the sidebands’ power difference. The device can also serve as a wavelength locker.

(Power (10GHz)-Power (–10GHz))/Power (0GHz)

5.2 Monitoring of timing alignment

129

0.2 Simulation Measurement 0.1

0.0

–0.1

–0.2

–20 –10 0 10 20 Frequency (GHz)

–30

–20

10 –10 0 Timing offset (ps)

20

30

FIGURE 5.2 Measured spectrum asymmetry due to timing misalignment.3

Copyright © 2002 OSA.

As shown in Figure 5.1, the signal is distorted due to timing misalignment. This will lead to the change in spectrum. Therefore, by measuring the signal spectrum, the information of timing alignment can be obtained. In Reference 4, a novel approach for aligning the pulse carver and data modulator in a 9.9532-Gb/s, OC-192 optical transmitter using dual MZMs is presented. A mathematical explanation for the spectral phenomenon is presented and shown to agree quite well with measurement and simulation results. When there is misalignment, it is shown that there is a spectral null at 0.64 bit rate, when the MZM is biased at the point of maximum optical transmission (Figure 5.3). A simple alignment detection is shown in Figure 5.4(a) to measure the radio frequency (RF) power of the first spectral null with a 2-GHz spectral width around the null frequency. The relationship between the measured RF power and the relative time delay between the data modulator and pulse carver is given in Figure 5.4(b). Around 7.5 dB excursion in the RF power is obtained when the relative delay between the carver and data modulator is changed from the perfect alignment case to the maximum misalignment case. This technique relies solely on the photodiode-detected microwave power spectrum of the transmitted optical signal. As the timing misalignment is a slow process, low-speed analog-to-digital (A/D) and digital-to-analog (D/A) converters can be used. However, the proposed simple scheme is affected by the signal power variation in the transmission. To circumvent the problem, a more robust scheme that resembles a low-cost RF spectrum analyzer is also presented in Reference 4. A monitoring technique for the timing-misalignment for RZ-DPSK systems is proposed and demonstrated in Reference 5. The proposed alignment-monitoring module is composed of a polarizationmaintaining fiber (PMF), polarizer, and power meter. Due to the limited response of the phase modulator, the falling edge of a pulse and the rising edge of the subsequent pulse exhibit phase transition when the two pulses are encoded with two different phases. After propagating through a PMF, the phase transition period will induce a change of the degree of polarization (DOP), and thus a power change, when the signal passes through a polarizer. To achieve better resolution, the polarizer can be orthogonally aligned to the state of polarization (SOP) of the incoming signal; thus, reduction

CHAPTER 5 Timing misalignment monitoring

–20.0

–20.0

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(b)

FIGURE 5.3 Measured spectra for (a) aligned and (b) misaligned cases. The first spectral null occurs at around 6.4 GHz.4

BPF

Low-Speed A/D

LPF

Photo- Schottky diode detector

CPU Low-Speed D/A

fdata /2

VoltageControlled Phase Shifter To Carver

(a)

Average power in a 2-GHz bandwidth (dBm)

Copyright © 2003 IEEE.

–80 –81 –82 –83 –84 –85 –86 –87 –88 –89 200 220 240 260 280 300 320 340 360 380 Time delay (ps)

(b)

FIGURE 5.4 (a) Simple alignment-detection scheme. (b) Measurements: microwave monitoring of misalignment.4 Copyright © 2003 IEEE.

of the signal’s DOP will result in the increase of monitoring signal power. When there is no timing misalignment, the phase transition period resides in the space between two adjacent pulses. When timing misalignment occurs, the phase transition period will move toward the center of the leading pulse or trailing pulse, resulting in the increase of monitoring signal power due to the decrease in DOP. Both the simulation and measurement results show that the proposed technique can monitor the BER degradation caused by timing misalignment between the pulse carver and data modulator. Only a low-speed power meter is needed. The scheme, however, has limited MPDR (0.2 dB for 50-ps


131

misalignment), and is susceptible to noise and environmental changes. To avoid the polarization fluctuation at the output of the PMF, further control of interferometric stability is needed. To enhance the monitoring sensitivity, a monitoring scheme that employs a simple off-center optical filter is proposed to extract optical power for alignment monitoring in RZ-DPSK systems.7,8 The operation principle is based on the property that frequency chirp is induced at the phase transition edge after data modulation by a phase modulator, as shown in Figure 5.5(a). With the increased misalignment amount, the signal spectrum is broadened, and power nulls will appear in the signal spectrum with the nulls’ spacing of 1/T, the bit rate (Figure 5.5(b)). Thus, by filtering out a narrow slice from the edge of the signal spectrum, any misalignment-induced spectrum broadening will be translated into an increase in the output power from the optical filter. By monitoring the optical power at the optical filter output, the alignment status can be obtained. Compared with the previous monitoring scheme using degree of polarization,6 in which the MPDR is 0.2 dB, a larger MPDR of 3.35 dB is achieved for the same timing alignment range of half-bit period, thus achieving a higher monitoring sensitivity. The monitoring power depends on the filter bandwidth and frequency offset from the signal’s center wavelength. A maximum MPDR of 3.35 dB is obtained when the optical filter with a bandwidth of 0.22 nm is placed at a 0.5-nm offset from the signal center wavelength. This simple alignment monitoring scheme has the desirable features of high-speed operation, polarization independence, and possible integration with transmitter for synchronization feedback control. In Reference 9, another technique for monitoring the timing alignment between a pulse carver and a phase modulator in RZ-DPSK systems is proposed. To monitor the spectrum broadening caused by timing misalignment, an optical frequency discriminator and a microwave detector centered at one-half of the phase modulation data rate are used, as shown in Figure 5.6. The frequency

Phase transient region

40

(i)

20

...

Worst case

...

Best case

...

...

Magnitude (a.u.)

Data pattern

0

40

–15

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–10

–5

0

5

10

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(ii)

20 0

40

(iii)

20 0

Misalignment tolerance range (a)

f/f0 (b)

FIGURE 5.5 (a) Illustration of timing alignment between pulse carver and data modulator. (b) Calculated signal spectra with timing alignment between pulse carver and data modulator of (i) 0, (ii) 0.3, and (iii) 0.5 T in a 10-Gb/s RZ-DPSK system with 0.28-T pulsewidth.8 Copyright © 2005 IEEE.

132


Clock section

Data modulation

CW laser

Pulse carver

Phase modulator

Wavelength controller

Voltagecontrolled phase shifter

Controller

Tap coupler

NRZ data Clock

Optical RZ-DPSK receiver

|H( f )| 0.5 FSR

Optical frequency discriminator H(f)

f

RF power monitoring

[dV(t)]pk-to-pk

BPF@fc /2

(a)

(b)

FIGURE 5.6 (a) Proposed setup for monitoring clock misalignment. (b) Frequency-to-intensity conversion characteristic of delay-and-add discriminator.9 Copyright © 2006 IEEE.

discriminator, which is a thin-film filter with a linear frequency transition range of 0.3 nm, converts the frequency chirp into power variation. The RF power at 5.3 GHz of the frequency discriminator output is measured. This timing-misalignment detection method can be used when the phase modulation is implemented by a dual-arm MZM or a phase modulator. Compared with aforementioned monitoring schemes, this polarization-independent method can achieve a much larger MPDR of 17.5 dB within a range of 40 ps of misalignment, and therefore better monitoring sensitivity. In addition to RZ-DPSK, this method can also be applied to other modulation techniques such as RZ- and carrier-suppressed RZ-OOK modulation, which use two optical modulators for data and clock. In Reference 10, the delay-tap synchronous waveform sampling technique is proposed and experimentally demonstrated to monitor the alignment between the pulse carver and the data modulator in RZ-OOK systems. The proposed alignment monitoring scheme is based on a delay-tap, asynchronous waveform sampling technique.11 The two-tap scatter plot obtained for timing alignment within 50 to 50 ps is shown in Figure 5.7(a). The features of the plot are analyzed and two parameters, d and t, are defined to capture the information of timing misalignment. The parameter d represents the average distance of every sample point from the diagonal, while t represents the average angle of the sample points from the origin. Only the top-right part of the plot is considered, so as to reduce the computation time and the influence from the vertical and the horizontal edges. Figure 5.7(b) depicts the value of d and t for various misalignments. By using both d and t values derived from the measurement, the timing misalignment can be uniquely determined. This proposed scheme is able to determine both the misalignment magnitude and the sign of misalignment. The scheme is successfully demonstrated for monitoring the timing misalignment of a 10-Gb/s RZ-OOK transmitter. As it is not based on the frequency chirp of the modulator, the scheme is also applicable to the dual-drive Mach-Zehnder (MZ) modulator that has a very small chirp.


–30 ps

–40 ps

–50 ps

+30 ps

+40 ps

+50 ps

133

0 ps

1.8e–3

0.86

1.6e–3

0.84 0.82

1.4e–3

0.80 1.2e–3 0.78 1.0e–3

0.76

8.0e–4 6.0e–4 –50 –40 –30 –20 –10 0 10 20 30 40 Timing misalignment (ps) (b)

0.74 0.72 50

t, average angle of sample points (rad)

d, deviation from diagonal (a.u.)

(a)

FIGURE 5.7 (a) Two-tap plot for various modulation timing misalignment. (b) Measured d and t parameters for various timing misaligments.10 Copyright © 2006 IEEE.

Two-photon absorption (TPA) is a nonlinear process that can be used to monitor waveform distortion.12 At the same average input power, the efficiency of TPA processes in Si-APD depends on the optical waveform. Thus, TPA can be employed for monitoring the waveform distortion generated by the timing misalignment of the pulse carver and data modulation signals. The misalignment monitoring in optical transmitters with 20-Gb/s RZ-OOK and CSRZ-OOK modulations is demonstrated.13 The TPA efficiency decreases when the misalignment exceeds 10 ps. For RZ-OOK, the monitoring signal is around 3-2.7 (r.u.) for misalignment of 10 to 28 ps, whereas for CSRZ-OOK, it is 235-222 (r.u.) for misalignment from 10 to 28 ps. Within 10 ps, the TPA efficiency is less sensitive to misalignment variation. Since this monitoring scheme is not

134


wavelength dependent, packaging it in MZMs has been proposed. No additional optical loss is incurred. The S-APD is quite sensitive and the destructive port of the modulator has sufficient power to be used for monitoring. Low-cost and low-speed electronics can be used to monitor high-speed data signals, as the scheme is data-rate independent. A few schemes to monitor the timing alignment have been described in this section. To summarize, in Table 5.1, the comparison of different schemes is provided based on the assessment of monitoring schemes as discussed in Section 5.1.

5.2.2 Synchronization for phase remodulation Synchronized phase remodulation to erase the original phase information and write the new phase information for phase-encoded modulation formats is a versatile signal processing technique for various applications in optical networks. The applications include optical label swapping in optical networks, provisioning of the optical virtual private network in a passive optical network (PON), the remodulation of the downstream signal for upstream transmission in wavelength-division multiplexing PON (WDM-PON), and the generation of spectral-efficient orthogonal modulation format.14–16 The second-phase modulator for phase remodulation needs to be in synch with the firstphase modulator; otherwise, waveform distortion is induced, leading to the change in the signal’s optical spectrum. In Reference 17, a simple and polarization-insensitive monitoring scheme for synchronized phase remodulation is demonstrated by using offset filtering with a narrowband optical bandpass filter (OBPF). The monitoring for the phase remodulation of an NRZ- and RZ-DPSK signal with a data rate of 10.61 Gb/s is investigated. To maximize the MPDR, the central wavelength of the OBPF needs to be optimized. Experimental results show that MPDR of 1.2 and 3.8 dB are obtained for NRZ-DPSK, and RZ-DPSK formats at an OBPF wavelength offset of 0.45 and 0.4 nm, respectively.

5.2.3 Synchronization for I/Q data and data/pulse carver Multilevel formats, such as differential quadrature phase-shift keying (DQPSK), are highly desirable for high-speed, long-haul transmission systems due to their tolerance of fiber nonlinearity and better spectral efficiency.2,18 In DQPSK, the in-phase (I) and quadrature-phase (Q) data are simultaneously transmitted in a single symbol time. When a pulse carver is used for generating RZ pulses, timing alignment monitoring for the synchronization of pulse carver and data modulator is needed. In addition, synchronization for the two orthogonal data channels is also necessary. In Reference 19, the authors experimentally demonstrate a technique for monitoring the time misalignment of in-phase/ quadrature-phase (I/Q) data streams and pulse carver/data in a 20-Gb/s RZ-DQPSK transmitter, as shown in Figure 5.8. The scheme is based on the measurement of the RF clock-tone power at 10 GHz (for I/Q data misalignment) and the low-frequency power at 600 MHz (for carver/data misalignment). As shown in Figure 5.9(a), an MPDR of 18 dB can be achieved for the monitoring of I/Q data misalignment by measuring the 10-GHz RF power. The monitoring scheme is more effective when the I/Q misalignment is more than 20 ps. For the carver/data misalignment, an MPDR of 6 dB is achieved when I/Q data misalignment is 0%, as shown in Figure 5.9(b). When there is 20% of I/Q data misalignment, the MPDR for the carver/data misalignment is degraded.

Table 5.1 Comparisons of Monitoring Schemes for Timing Misalignment Kim6

Sinsky4

Ku7 and Lu8

Tsai9

Ku10

Tian13

Yes

No

No

No

No

Yes

No

0.4 dB

0.2 dB

7.5 dB

3.35 dB

t: 0.73 to 0.84

0.46 dB (RZ) and 0.27 dB (CSRZ) for misalignment from 10 to 28 ps 28 to 28

30 to 30

50 to 50

50 to 50

50 to 50

17.5 dB (theoretical: 1 limited by phase detector noise) 40 to 40

10-Gb/s RZ-OOK No

10-Gb/s RZ-DPSK No

10 Gb/s RZ-OOK Yes

10.61-Gb/s RZ-DPSK No

10.61-Gb/s RZ-DPSK Yes

10-Gb/s OOK

No

Yes

No

No

No

No

d: (8 to 13.5) 104 50 to 50

Yes

20-Gb/s RZ-OOK and CSRZ-OOK No; data-rate independent No


Detection of the sign of misalignment Monitoring power dynamic range Misalignment range (ps) Data modulation High-speed receiver needed? Polarization dependent

Kang3

135

136


Data I

Clock DQPSK

RZ-DQPSK

CW π/2

MZM

Data Q

RF power

RZ-DQPSK RF spectrum (direct detection)

RF power

DQPSK RF spectrum (direct detection)

Rs I/Q aligned

Rs freq misaligned

Rs carver aligned

Rs freq misaligned

FIGURE 5.8 Misalignment monitoring of an RZ-DQPSK transmitter.19


0%

Normalized RF Power at 600 MHz (dB)

Normalized RF power at 10 GHz (dB)

0 –4 –8 50 ps/div –12 –16 50 ps/div –20 –50 –40 –30 –20 –10 0 10 20 30 40 I/Q data misalignment (% symbol period)

(a)

50

20%

–20% I/Q data misalignment

7 50 ps/div 6 5 4 3 2 50 ps/div 1 0 –1 –2 –50 –40 –30 –20 –10 0 10 20 30 40 50 Carver/data misalignment (% symbol period)

(b)

FIGURE 5.9 Monitoring signal power for (a) I/Q data misalignment and (b) carver/data misalignment.19 Copyright © 2008 IEEE.

5.2.4 OTDM clock recovery using timing misalignment of data pulses Albeit timing misalignment between the pulse carver and data modulator incurs power penalty and generally is an issue that needs to be resolved, timing misalignment can also be employed for useful applications. One example is to use the timing misalignment for the base-rate clock recovery in optical time-division multiplexed (OTDM) systems, where the line-rate clock is n times of the base rate with n being the number of TDM channels. In Reference 20, a base-rate clock recovery

5.3 Investigation of the effects of timing misalignment

137

technique is analyzed and demonstrated. In the proposed scheme, an electrical clock is extracted from an ultra-high-speed OTDM RZ signal with a detector and a bandpass filter. The basic idea is that when there is a small misalignment between adjacent pulses in the transmitted data, a subharmonic tone at the base-rate frequency will be generated. The base-rate tone is recovered as a clock signal at the receiver. A clock recovery circuit is experimentally demonstrated for the demultiplexing of 40- and 80-Gb/s optical TDM systems to 10 Gb/s. The effect of filter bandwidth, word length, and strength of timing shift on the received timing jitter are investigated. It is shown numerically that correlated TDM channels will decrease the jitter of the recovered clock considerably.

5.3 INVESTIGATION OF THE EFFECTS OF TIMING MISALIGNMENT In the previous section, various monitoring schemes for timing misalignment are discussed. In this section, the investigations of the effects of timing misalignment for some modulation formats are presented.

5.3.1 Clock/data synchronization in CPFSK systems Continuous-phase frequency-shift keying (CPFSK) is a promising modulation format because it has a compact spectrum and its receiver sensitivity can be improved with differential detection. A CPFSK modulator is implemented by embedding two MZMs in each arm of a main modulator.21 In the CPFSK modulator, synchronization between the clock and data signals is required, similar to the case of pulse carver and data modulation in RZ-DPSK transmitters. In Reference 22, a synchronous control technique for external optical modulation for CPFSK at 10 Gb/s is demonstrated. The modulator is shown in Figure 5.10(a). The simulated modulation spectral for CPFSK and BPSK is given in Figure 5.10(b) for comparison. In the proposed CPFSK scheme, the FSK signal in the upperor lower-sideband state synchronously shifts to the other state at the time when their phases are the same. Timing control between the sinusoidal clocks and the data signal is required to enable a synchronous control that gives rise to continuous-phase modulation. The delay between the clock and baseband signal is adjusted to achieve the optimal condition for the CPFSK modulation by monitoring its optical spectrum. The accuracy of the timing control required for the synchronization control is investigated. The timing misalignment causes phase discontinuity at the frequency shift and results in eye closure of the received signal. Experimental results show that the allowable timing misalignment for less than 1-dB power penalty of the receiver sensitivity is more than 25 ps, which is 25% of each bit period.

5.3.2 Misalignment between pulse carver/data modulator in RZ-DPSK systems The power penalty induced by the timing misalignment between pulse carver and data modulator for RZ-DPSK systems has been shown in the previous sections in various misalignment monitoring schemes. In Reference 23, the effect of the pulse shape is investigated. A 10-Gb/s Gaussian-shaped pulse train with a 33% duty cycle and raised-cosine NRZ data with the pulse shape sharpness factor a ¼ 0, 0.4, and 0.8 are assumed. It is shown that for an RZ-DPSK system, the timing misalignment– induced distortion (TMID) can be generalized as intersymbol interference (ISI). For a raised-cosineshaped NRZ pulse, the misalignment tolerance range would reduce from 25 to 15 ps in 10-Gb/s RZDPSK systems when a increases from 0 to 0.8. As expected, a narrower pulse is more tolerant of

138


MZ-a MZ-c Laser

MZ-b

RF-a

RF-b π/2

Intensity, 20 dB/div

Modulation spectra (simulated) CPFSK

DATA

Wavelength offset, 0.5 nm/div

Δφb

(b)

Phase locked Sinusoidal clock

Baseband data

f0 = B/2 [Hz]

B [bit/s]

(a)

Intensity, 20 dB/div

cf BPSK (DPSK)

Wavelength offset, 0.5 nm/div (c)

FIGURE 5.10 (a) Basic structure of CPFSK modulator with synchronous control. Numerically calculated optical modulation spectra for (b) CPFSK and (c) BPSK (DPSK).22 Copyright © 2006 IEEE.

timing misalignment. The reason that the power penalty depends on parameter a is twofold. First, the temporally less-confined NRZ pulses with larger a values would experience more ISI. Second, when timing is misaligned, much energy leaks into the phase transition region, which enhances performance sensitivity to a. As TMID can be generalized as ISI, it suggests that TMID can be compensated by employing electronic equalization.

5.3.3 Misalignment between ASK and DQPSK modulation in ASK/DQPSK orthogonal modulation systems Recently, multilevel optical orthogonal modulations combining amplitude-shift keying and differential quadrature phase-shift keying (ASK/DQPSK) have drawn much attention as they exhibit better spectral efficiency, which helps alleviate some of the transmission distortions. In Reference 24, the tolerance for timing misalignment between 10-Gb/s ASK data and 20-Gb/s DQPSK data in ASK/DQPSK orthogonal modulation systems is investigated. Three different ASK formats are used, namely NRZ, RZ, and inverse-RZ. For DQPSK, the modulation can be implemented by a phase modulator (PM) or MZM, which leads to different phase and intensity characteristics, as shown in

5.3 Investigation of the effects of timing misalignment

Intensity

139

Phase PM

PM

MZM

MZM

FIGURE 5.11 Modulation characteristics of PM and MZM.24


Figure 5.11. It is shown that impairment from timing misalignment strongly depends on ASK formats and DQPSK phase-modulation methods (PM or MZM). Monte-Carlo simulations were performed to investigate the dependence. In Figure 5.12, it is shown that DQPSK modulation methods influence the timing misalignment sensitivity of ASK 3

Receiver power penalty (dB)

3 2.5 2 1.5 1

NRZ–ASK/DQPSK

RZ–ASK/DQPSK

2

MZM, DQPSK MZM, ASK PM, DQPSK PM, ASK

MZM, DQPSK

1

MZM, ASK 0

0.5

PM, DQPSK PM, ASK

–1

0 –2

–0.5 –1

–50 –40 –30 –20 –10 0 10 20 30 40 50 Misaligned time t0 (ps)

–3


(a)

(b)

Receiver Power Penalty (dB)

3 2

IRZ–ASK/DQPSK

1 0 –1 –2

MZM, DQPSK

MZM, ASK PM, ASK

PM, DQPSK –3


(c)

FIGURE 5.12 Receiver power penalty versus t0 for various ASK formats and DQPSK modulation methods. (a–c) sampling phases are optimized.24 Copyright © 2006 IEEE.

140


signals, but not of DQPSK signals. For instance, as shown in Figure 5.12(a), ASK data experienced large power penalty variation when DQPSK data were using MZM for modulation, but not when using PM. This can be attributed to the waveform distortion from the intensity characteristic of the DQPSK, as shown in Figure 5.11. In contrast, ASK formats influence the DQPSK signal’s timing misalignment sensitivity, but not the ASK signal’s. DQPSK is more robust to timing misalignment when ASK is using IRZ format. The results also show that the performance of IRZ-ASK/DQPSK can be improved by using certain timing misalignments, as depicted in Figure 5.12(c).

5.4 MITIGATION OF TIMING MISALIGNMENT Monitoring of timing misalignment and effects of timing misalignment for different modulation formats were discussed in the previous sections. In this section, a few schemes that alleviate the impairment induced by timing misalignment for different applications are presented.

5.4.1 Hybrid OTDM scheme for demultiplexing with better timing misalignment tolerance In an OTDM system, as the data rate increases, demultiplexing of optical tributary channels becomes more and more challenging. It would be desirable to allow channel demultiplexing with a larger timing misalignment tolerance or a relaxed switching window. In Reference 25, a novel OTDM scheme with hybrid RZ-ASK/RZ-DPSK formats that eases the demultiplexing difficulty is proposed and investigated. The RZ-ASK channels are interleaved with the RZ-DPSK channels in time domain. For an OOK channel in the proposed hybrid OTDM signal, the crosstalk from adjacent DPSK channels is a constant power, which mainly alters the detection threshold. Similarly, for a DPSK channel in a hybrid OTDM signal, the demultiplexing with a relaxed switching window will gate part of the adjacent OOK bits. After DPSK demodulation with delayed interferometer (DI), the gated part of the OOK bit will destructively interfere with that of its previous bit; thus, it will vanish or become onequarter of its original power, depending on whether these two consecutive bits are the same or not. For 40-Gb/s OTDM systems, the tolerances to demultiplexing timing misalignment are improved by 180% and 70%, for RZ-ASK and RZ-DPSK channels, respectively. In Reference 26, the proposed scheme is applied to an 84.88-Gb/s OTDM system with a channel data rate of 10.61 Gb/s. Compared with the conventional OTDM system with homogenous modulation format for different OTDM channels, the demultiplexed channel in a hybrid OTDM signal suffers from less degradation caused by the possible crosstalk from the adjacent channels. 84.88- to 10.61-Gb/s hybrid OTDM demultiplexing is achieved with a relatively wide switching window, which cannot be realized by using the conventional OTDM.

5.4.2 Novel remodulation scheme for colorless high-speed WDM-PON without remodulation synchronization Wavelength-division multiplexing passive optical network (WDM-PON) has aroused much attention for next-generation broadband access architecture, due to its large dedicated bandwidth per user and upgrade flexibility. A centralized light source is desirable in WDM-PON because such source in the central office (CO) eliminates the need of the wavelength-specific transmitters at the optical

5.5 Summary

141

network units (ONUs) and features cost reductions of wavelength management on the customer side. For previous schemes using downstream DPSK and upstream infinite-extinction ratio (ER) OOK, it is shown that despite its better back-to-back sensitivity, closed eye is exhibited for the upstream signal when there is no chromatic dispersion compensation and remodulation synchronization. At ONU, the downstream DPSK signal exhibits intensity fluctuation at the phase transition period, thus the upstream data at ONU needs to be synchronous with the downstream data during remodulation. In Reference 27, a remodulation scheme using downstream 10-Gb/s OOK and upstream 10-Gb/s DPSK is proposed for a 30-km-range colorless WDM-PON without dispersion compensation and remodulation synchronization. The downstream OOK signal has an ER of 4.9 dB, resulting in a 3.5-dB, back-to-back penalty. The upstream DPSK data are encoded using a phase modulator. After transmission, only 1-dB penalty for both 10-Gb/s downstream and 10-Gb/s upstream signals is demonstrated. It is shown that the proposed scheme is robust for remodulation misalignment, with the receiver sensitivity variation less than 0.6 dB.

5.4.3 Misalignment mitigation using MLSE equalizers As discussed in Reference 23, the distortion induced by timing misalignment between the pulse carver and data modulator can be generalized as intersymbol interference; thus, it can be mitigated by electronic equalization. A novel method using an electronic equalizer is proposed to mitigate the impairment from timing misalignment in RZ and carrier-suppressed RZ systems.28 Simulations results show that a maximum-likelihood sequence equalizer (MLSE) significantly reduces timing misalignment-induced power penalty and sampling phase sensitivity. Simultaneous mitigation of both timing misalignment-induced distortion and polarization-mode dispersion (PMD) using a single MLSE is also demonstrated. In Reference 29, the performance of maximum-likelihood sequence estimation (MLSE) receiver in the presence of the impairments from both the pulse carver/data modulator timing misalignment and PMD in optically amplified 10-Gb/s RZ systems are investigated. The results show that by using the MLSE receiver, the power penalty for the worst timing misalignment, where the eye is completely closed, is limited to 6 dB in the absence of PMD and 9 dB in the presence of the worst PMD. The investigation validates the effectiveness of an MLSE receiver for combating both timing misalignment and PMD with shared electrical devices, which therefore reduces the required number of compensation components.

5.5 SUMMARY Various timing alignment monitoring schemes have been presented in this chapter. For future optical networks, as the line rate increases, the timing alignment requirement becomes more and more stringent. Thus, considering the scalability of the monitoring schemes is essential. Most monitoring schemes exhibit a small monitoring power change, and thus limited resolution, when the timing misalignment is small. Although the power penalty is small when the timing misalignment is small, enhancing the monitoring sensitivity is desirable while simultaneously avoiding monitor range reduction. Another issue is the response time of monitoring. Some monitoring schemes take advantage of the slow change of timing misalignment due to environment variation, and therefore employ only low-speed detection circuits to simplify the design. A much faster monitoring scheme may be

142


necessary for future optical packet-switching networks. Considering WDM applications, it would be useful if multichannel timing misalignment monitoring schemes can be devised. Finally, it is always desirable to have a monitoring scheme that can facilitate the multi-impairment monitoring.30 It would be interesting to implement an integrated monitoring scheme that is capable of monitoring timing misalignment and other impairments simultaneously, such as wavelength detuning and power variation at the transmitter. In conclusion, various timing misalignment monitoring schemes were discussed and compared in this chapter. Performance metrics, including MPDR, monitoring range, data rate, complexity, and so on, were discussed to facilitate comparison. Although this chapter is mainly focused on the synchronization between pulse carver and data modulation, various investigations into the effect of timing misalignment and means to mitigate effects of timing misalignment are also presented. It is hoped that the information provided in this chapter will be useful for more general applications that require timing synchronization.

REFERENCES 1. Xu C, Liu X, Wei X. Differential phase-shift keying for high spectral efficiency optical transmissions. IEEE J Sel Top Quantum Electron 2004;10:281–93. 2. Gnauck AH, Winzer PJ. Optical phase-shift-keyed transmission. J Lightwave Technol 2005;23:115–30. 3. Kang I, Mollenauer L, Greene B, Grant A. A novel method for synchronizing the pulse carver and electroabsorption data modulator in RZ optical transmitters. In: Proc. OFC, paper ThCC4. Anaheim, CA; 2002. 4. Sinsky JH. High-speed data and pulse-carver alignment in dual Mach-Zehnder modulator optical transmitters using microwave signal processing. J Lightwave Technol 2003;21:412–23. 5. Monitoring alignment between pulse carvers and phase modulators in optical systems. United States Patent 6972842. 6. Kim H, Doerr CR, Pafchek R, Stulz LW, Bernasconi P. Alignment monitoring of the pulse carver and data modulator for RZ-DPSK systems. IEEE Photon Technol Lett 2003;15(11):1594–6. 7. Ku YC, Lu GW, Chan CK, Chen LK. Novel technique for modulation alignment monitoring in RZ-DPSK systems using off-center optical filtering. In: Proc. OFC, paper OWJ4. Anaheim, CA; 2005. 8. Lu GW, Ku YC, Chen LK, Chan CK. A novel technique for pulse-carver and data alignment monitoring in RZ-DPSK systems using off-center optical filtering. IEEE Photon Technol Lett 2005;17(3):711–3. 9. Tsai KT, Lu GW, Chen LK, Winston I, Way WI. Alignment monitoring technique for pulse carver and data modulator in RZ-DPSK systems using an optical frequency discriminator. IEEE Photon Technol Lett 2006;18(10):1119–21. 10. Ku YC, Chan CK. High-speed data and pulse-carver alignment in RZ-OOK systems using delay tap asynchronous waveform sampling. In: Proc. ECOC, paper Tu4.2.2. Cannes, France; 2006. 11. Dods SD, and Anderson TB. Optical Performance Monitoring Technique Using Delay Tap Asynchronous Waveform Sampling. In: Proc. OFC, paper OThP5. Anaheim, USA; 2006. 12. Tian C, Kinoshita S. Waveform monitoring with polarization-dependence-eliminated two-photon absorption in Si-APD in high-speed WDM systems. In: Proc. ECOC, paper We4.P.70. Glasgow, UK; 2005. 13. Tian C, Naito T. Data and pulse-carver alignment in high-speed optical transmitters monitored with two-photon absorption in Si-APD. In: Proc. SPIE, paper 63532Y. Soc Photo Opt Instrum Eng 2006;6353. 14. Hung W, Chan CK, Chen LK, Tong F. A bit-serial optical packet label swapping scheme using DPSK encoded labels. IEEE Photon Technol Lett 2003;15(11):1630–2.

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15. Tian Y, Su Y, Yi L, Leng L, Tian X, He H, et al. Optical VPN in PON based on DPSK erasing/rewriting and DPSK/IM formatting using a single Mach-Zehnder modulator. In: Proc. ECOC, paper Tu4.5.6. Cannes, France; 2006. 16. Pun S-S, Chan C-K, Chen L-K. Demonstration of a novel optical transmitter for high-speed differential phase shift keying/inverse return-to-zero orthogonally modulated signals. IEEE Photon Technol Lett 2005;17(12):2763–5. 17. Zhao J, Ku YC, Chen LK. Simple monitoring technique for synchronised phase remodulation using narrowband optical filtering. IEE Electron Lett 2007;43(6):63–4. 18. Kim H, Essiambre R-J. Transmission of 820 Gb/s DQPSK signals over 310-km SMF with 0.8-b/s/Hz spectral efficiency. IEEE Photon Technol Lett 2003;15:769–71. 19. Wu X, Christen L, Zhang B, Peng W-R, Yang J-Y, Zhang L, et al. Synchronization monitoring of I/Q data and pulse carving misalignment for a parallel-type RZ-DQPSK transmitter by measuring RF clock tone/low frequency power. IEEE Photon Technol Lett 2008;20(24):2138–40. 20. Hansryd J, Andrekson PA, Bakhshi B. Prescaled clock recovery based on small timing misalignment of data pulses. J Lightwave Technol 2001;19(1):105–13. 21. Kawanishi T, Sakamoto T, Shinada S, Izutsu M, Higuma K, Fujita T, et al. High-speed optical FSK modulator for optical packet labeling. In: Proc. OFC, paper PDP16. Los Angeles, CA; 2004. 22. Sakamoto T, Kawanishi T, Miyazaki T, Izutsu M. 10-Gb/s external modulation in optical CPFSK format. IEEE Photon Technol Lett 2006;18(8):968–70. 23. Zhao J, Chen LK, Chen CK. Performance degradation induced by pulse carver/data modulator misalignment in RZ-DPSK systems. In: Proc. IEEE LEOS, paper JThE62. Baltimore, MD; May 2005. 24. Zhao J, Chen LK, Chan CK. Tolerance of misalignment between ASK and DQPSK modulation in ASK/ DQPSK orthogonal modulation systems. In: Proc. OECC, paper 5F3-3. Kaohsiung, Taiwan; 2006. 25. Deng N, Chan CK. Enhanced tolerance to demultiplexing misalignment in an OTDM system with hybrid RZ-ASK/DPSK formats. In: Proc. OFC/NFOEC, paper JWA51. Anaheim, CA; March 2007. 26. Deng N, Chan CK, Chen Lk. A hybrid OTDM scheme with enhanced demultiplexing performance. IEEE Photon Technol Lett 2007;19(19):1454–6. 27. Zhao J, Chen LK, Chan CK. Novel re-modulation scheme to achieve colorless high-speed WDM-PON with enhanced tolerance to chromatic dispersion and re-modulation misalignment. In: Proc. OFC/NFOEC, paper OWD2. Anaheim, CA; March 2007. 28. Zhao J, Chen LK, Chan CK. Mitigation of timing misalignment-induced distortion using electronic equalizer in RZ/CSRZ systems. IEEE Photon Technol Lett 2005;17(5):1106–8. 29. Zhao J, Chen LK, Chan CK. Maximum likelihood sequence estimation in the presence of timing misalignment and polarization mode dispersion in optically amplified return-to-zero systems. In: Proc. IEEE ICC, paper CT20-7. Istanbul, Turkey; June 2006. 30. Chen LK, Chan CK, Lu GW, Ku YC, Lin CL. Optical performance monitoring and network diagnosis in reconfigurable optical networks. In: Proc. APO 2007, paper 6784-54. Wuhan, PRC; November 2007.


CHAPTER

6

Optical performance monitoring based on asynchronous amplitude histograms ,{

,{

Ruben S. Luis* , Liliana Costa*, Antońio Teixeira* , Paulo Andre´*

,}

*

Instituto de Telecomunicaço˜es, Campus de Santiago, Aveiro, Portugal Center of Volcanology and Geological Risk Assessment, University of Azores, Ponta Delgada, Portugal { Departamento de Electrońica, Telecomunicaço˜es e Informa´tica, University of Aveiro, Campus de Santiago, Aveiro, Portugal } Departamento de Fı´sica, University of Aveiro, Campus de Santiago, Aveiro, Portugal

{

6.1 INTRODUCTION Optical signals traveling along a network are fundamentally degraded by amplified spontaneous emission (ASE) noise due to optical amplification, crosstalk, and signal distortion as a result of transmission through optical components and optical fibers.1 With the rapid increase of transmission bit rates and the channel density in wavelength-division multiplex (WDM) systems, the impact of signal distortion or crosstalk on the quality of the optical signals may equal or even surpass the currently dominant impact of ASE noise.1 As such, there is an urgent need for the development of optical monitoring systems (OMSs), which effectively distinguish and quantify the impact of these forms of degradation. Generally, the development of OMS is performed subsequent to prior advancements in transmission technologies or components. Furthermore, the cost of advanced OMS often imposes development on a requirement basis—that is, OMSs are usually developed as support subsystems for specific applications, when necessary. For this reason, OMSs are usually one step behind the development of mainstream optical transmission systems. It is not uncommon that a given optical monitoring technology only reaches maturity after the transmission technology that it was initially developed for is dropped in favor of alternative advancements. Although this is not a rule, one may consider that the deployment of commercial optical transmission systems, which requires an established maturity of all subsystems, has been performed with limited or simply inexistent optical monitoring technology. Given the rapid increase in recent years of requirements for optical transmission systems in terms of bit rate, distance, and overall flexibility, one may expect that this cycle will be broken in the near future as the OMS becomes an enabler of optical transmission systems. Hence, the development of OMSs since the implementation of WDM systems has brought a large number of solutions, which is steadily increasing.1 In this context, it may be assumed that the most interesting optical monitoring technologies are those that can be easily adapted or upgraded to support the rapid advances of other technologies used

© 2010 Elsevier Inc. All rights reserved. Doi: 10.1016/B978-0-12-374950-5.00006-7

145

146

CHAPTER 6 OPM based on asynchronous amplitude histograms

in optical transmission systems. Following this premise, OMS based on the analysis of asynchronous histograms have attracted significant attention due to their simplicity and remarkable flexibility.2 Asynchronous histograms may be acquired by sampling the signal under analysis at an arbitrary rate, much lower than the symbol rate, which avoids the need for clock recovery systems. This potentially reduces the overall cost of the monitor and gives it flexibility by allowing the application of the same monitor to signals with arbitrary bit rates. Besides the simplicity of the histogram acquisition system, the analysis of asynchronous histograms can be performed using relatively simple algorithms, which may be implemented via software. This allows improving and upgrading the OMS indefinitely, simply by replacing its firmware rather than its hardware. To illustrate the usage of asynchronous histograms, consider Figures 6.1(a) and (b), which show a comparison between the simulated synchronous and asynchronous eye diagrams of a non-return-tozero (NRZ) signal and the corresponding histograms. In the case of the histogram acquired synchronously, the considered samples are located within an interval of 10% of the bit period around the center of the eye diagram. As shown in Figure 6.1(a), the synchronous histogram of the signal presents two main peaks corresponding to the samples acquired during the mark and space symbols. The statistical distribution of the samples around the peaks reflects the noise distributions associated with the corresponding symbols. In the case of the asynchronous histogram, one may also distinguish two main peaks corresponding to the mark and space symbols, similar to the case of the synchronous histogram. However, it is not possible to entirely dissociate the samples corresponding to the mark and space Synchronous histogram Electrical current (a.u.)

Electrical current (a.u.)

Synchronous eye diagram

Time (a.u.)

Occurrences (a) Asynchronous histogram



Asynchronous eye diagram

Time (a.u.)

Occurrences (b)

FIGURE 6.1 (a) Synchronous and (b) asynchronous eye diagrams and corresponding histograms of an NRZ signal.

6.1 Introduction

147

symbols. This is resulted from the large number of samples located between the two peaks, which have been acquired during the transitions between symbols, also referred to as crosspoint data. A study of the changes in the shape of asynchronous histograms as a result of optical impairments was first presented by Hanik et al.2 To illustrate this impact, consider Figures 6.2(a–c), which present examples of asynchronous histograms degraded by different levels of ASE noise, intraband crosstalk, and group velocity dispersion (GVD), respectively. In the first case, it is shown that decreasing the optical signal-to-noise ratio (OSNR) broadens and reduces the magnitude of the peaks corresponding to the mark and space symbols. This is particularly evident for the mark symbol, where the beating between signal and ASE noise is stronger. Note that the magnitude of the crosspoint data is not significantly affected when varying the OSNR. In the case of Figure 6.2(b), the OSNR is maintained and the amount of intraband crosstalk is increased by combining the original signal under analysis with a delayed replica. The impact of intraband crosstalk manifests as an emerging floor around the mark symbol. 0.125

0.16

S/N = 31dB (BER s2OMS :

(6.9)

When taking into consideration Equation (6.8), the condition of Equation (6.9) becomes ai > s2OMS osnrSIG =bsp ;

(6.10)

which implies that the proposed approximation is only valid above a given power level or below a maximum measurable osnrSIG. These limits are conditioned by the variance of the electrical noise generated by the OMS, in agreement with the general conditions for OSNR measurements predicted in Reference 29. The main drawback of this method is the requirement of a noiseless reference signal. The corresponding reference asynchronous histogram (RAH) may be easily acquired in laboratory conditions but would be difficult to obtain in a field installation. Consider now the case of a reference histogram acquired from a signal with finite OSNR. The noise affecting the reference signal then becomes characterized by a signal-ASE noise beat variance given by s2REF ðai Þ ¼ bsp ai =osnrREF . The initial value of OSNRREF may be measured at a calibration stage, using external equipments when installing the OMS in the field. Alternatively, the output of the network planning may be used to estimate this value. Subsequent histogram acquisitions will reflect the changes of OSNR of the signal under analysis with respect to OSNRREF. Assume the case where osnrSIG < osnrREF —that is, the signal under analysis presents a higher degradation than the reference signal. If this is the case, the method proposed by Equation (6.7) may be used to produce the estimated histogram by including the impact of additional noise on the reference histogram to approximate the measured histogram, as represented in Figure 6.12. In this case, the match between the measured and the estimated histograms will be found when the estimated variance of the signal-ASE beat noise reflects the difference between the corresponding noise variances of the signal under analysis and the reference signal—that is, 1 1 osnrREF : s2EST ðai Þ s2SIG ðai Þ s2REF ðai Þ ¼ bsp ai ½osnrSIG

(6.11)

Finally, assuming that when a match between the measured and the estimated histogram is found, we have osnrSIG osnrEST, yielding 1 1 s2EST ðai Þ ¼ bsp ai ½osnrEST osnrREF ;

(6.12)

which replaces Equation (6.8) for the implementation of the novel method. Note that if osnrREF is known from the calibration stage, the only unknown variable is osnrEST. The use of Equations (6.7) and (6.12) to produce the estimated histogram using a reference histogram with finite OSNR higher than the OSNR of the signal under analysis will be referred to here as method A. Similar to the case of a reference histogram with very high OSNR, the validity of the approximation used in method A requires that the variance of the signal-ASE noise beat affecting the signal under analysis is higher than the variance of the noise affecting the reference

6.2 Monitoring techniques based on analysis of asynchronous histograms

159

RAH presents finite OSNRREF < OSNRSIG

Reference asynchronous histogram, hREF,i Estimated histogram, hEST,i, presenting estimated OSNR, osnrEST Linear superposition of Gaussian distributions centered in ai with variance s 2EST (ai)

Fitting using nonlinear minimization method

Measured histogram, hMEA,i, presenting unknown OSNR, osnrSIG

FIGURE 6.12 Diagram of histogram estimation method A.

signal added to the variance of the OMS noise, assuming that the latter may be approximately described by a Gaussian distribution. This is represented by the condition s2SIG ðai Þ > s2REF ðai Þ þ s2OMS :

(6.13)

Note that for very high osnrREF, the contribution of the ASE noise affecting the reference signal at the OMS input may be neglected in comparison with the contribution of the OMS noise. As such, the validity condition of Equation (6.13) may be written as osnrSIG < bsp ai =s2OMS . This implies that the range of method A depends directly on the power at the OMS input. However, when reducing osnrREF the impact of the OMS noise becomes negligible and the validity condition may be written simply as osnrSIG < osnrREF. Note that the latter is independent of the power at the OMS input, suggesting that reducing osnrREF allows an increase of the sensitivity of the OMS at the cost of reducing the maximum measurable OSNR. Consider now the case when the signal under analysis is less degraded than the reference signal—that is, osnrSIG < osnrREF. When using method A, the nonlinear minimization algorithm will diverge to an arbitrarily high value of osnrEST, corresponding to the lowest possible value of s2EST ðai Þ ¼ s2REF ðai Þ. As such, the condition of failure of method A may be identifiedPwhen the distance between the b 2 ¼ Nn¼1 jhREF;n hEST;n j2 , converges estimated and reference histograms, defined in this work as dRE to zero. Although it is not possible to calculate an estimate of the measured histogram by including the impact of ASE noise in the reference histogram, as proposed for method A, it is possible to do just the opposite. If the signal under analysis is less degraded by ASE noise than the reference signal,

160


MAH presents OSNRSIG < OSNRREF

Measured asynchronous histogram, hMEA,i, presenting unknown OSNR, osnrSIG Estimated histogram, hEST,i

Linear superposition of Gaussian distributions centered in ai with variance s 2EST (ai)

Fitting using nonlinear minimization method

Reference asynchronous histogram, hREF,i, presenting known OSNR, osnrREF

FIGURE 6.13 Diagram of histogram estimation method B.

we may calculate an estimate of the reference histogram by including the impact of ASE noise in the MAH, as represented in Figure 6.13. The amount of ASE noise that has to be included to find a match will allow an estimation of the osnrSIG. This method will be referred here as method B. In this case, Equation (6.7) becomes " # Nb X hMEA;i ðan ai Þ2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp 2 ; (6.14) hEST;n ¼ 2sEST ðai Þ 2ps2EST ðai Þ i¼1 where hMEA,i represents the measured histogram. In addition, considering that the roles of the reference signal and the signal under analysis are swapped, Equation (6.12) will become 1 1 osnrEST : s2EST ðai Þ ¼ bsp ai ½osnrREF

(6.15)

Equations (6.14) and (6.15) allow estimating the OSNR of the signal under analysis when it is lower than the OSNR of the reference signal. Similar to Equation (6.13), the validity of the approximation used to estimate the reference histogram is given by s2REF > s2SIG þ s2OMS :

(6.16)

Note that for high osnrSIG, the impact of the ASE noise degrading the signal under analysis is negligible in condition of Equation (6.16), and the validity condition becomes osnrREF < bsp ai =s2OMS . This implies that the maximum osnrREF is conditioned by the power at the OMS input. Furthermore,

6.2 Monitoring techniques based on analysis of asynchronous histograms

161

it indicates that the reachable OSNR range may be increased by reducing osnrREF, similar to method A. For low osnrREF, the OMS noise is negligible and the validity condition simply becomes osnrSIG > osnrREF. The remaining problem with the proposed methods is the decision between using method A or B. Since osnrSIG is usually unknown, it may not be compared with osnrREF in order to select the appropriate method. As such, the solution is to initially use method A and test its condition of failure, defined previously. If the value of dRE converges to zero, one may assume that osnrSIG < osnrREF. As such, a new calculation will be required using method B. To validate the previously described method, consider the OMS presented in Figure 6.14. A pseudo-random binary sequence of 214 1 NRZ symbols at 40 Gb/s is used as the signal under analysis and fed into the OMS. The monitoring system will consist of an unsynchronized digital oscilloscope equipped with an optical front end with a bandwidth of 65 GHz to obtain the asynchronous amplitude histograms. In addition, the signal at the OMS input is preamplified using an EDFA with a noise figure of approximately 6 dB. An optical filter with a bandwidth of 0.6 nm is used after the preamplifier to limit the optical noise. Each histogram will be computed by acquiring 106 asynchronous samples of the signal. We begin by validating the OSNR estimation method using NRZ signals without preamplification in the OMS. The reference signal will be obtained in nearly noiseless conditions, with an OSNR of 28 dB. Figures 6.15(a) and (b) present examples of the measured histograms for OSNRSIG ¼ 14, 20, and 30 dB, as well as the reference histogram with OSNRREF ¼ 28 dB. Average powers at the input of the OMS of 0 dBm and 5 dBm have been considered. In the first case, the impact of ASE noise is clearly distinguishable on the shape of all presented histograms, particularly in the region of the histograms corresponding to the mark level. When the power at the OMS input is reduced to 5 dBm [see Figure 6.15(b)], the impact of the electrical noise, generated at the oscilloscope receiver, and the quantization noise, resulting from the limited resolution of the analog-to-digital converter (ADC) of the oscilloscope, becomes evident from the broadening of the peaks of the mark and space symbols, and the spikes of the histograms, respectively. In these conditions, the histogram obtained for OSNRSIG ¼ 30 dB is indistinguishable from the reference histogram with OSNRREF ¼ 28 dB. Figures 6.16(a) and (b) present the dependence of OSNREST on OSNRSIG, considering average powers of 0 dBm and 5 dBm at the input of the OMS, respectively, for OSNRREF ¼ 16 dB, 22 dB, and 28 dB. For a qualitative analysis, the insets of Figures 6.16(a) and (b) present eye diagrams of the signal at the oscilloscope input.

Optical monitoring system Signal under analysis 40-Gb/s NRZ

EDFA Optical filter

Digital oscilloscope

FIGURE 6.14 Schematic diagram of OMS for OSNR evaluation using asynchronous histograms.


OSNRSIG = 30 dB

OSNRSIG = 14 dB OSNRSIG = 20 dB

OSNRSIG = 30 dB

Occurrences (a.u.)

OSNRREF = 28 dB

Occurrences (a.u.)

162

OSNRREF = 28 dB

OSNRSIG = 14 dB OSNRSIG = 20 dB

Electrical current (a.u.) (a)

Electrical current (a.u.) (b)

FIGURE 6.15 Asynchronous histograms of reference signal considering an OSNR of 28 dB (dashed line) and signal under analysis with OSNR values of 14, 20, and 30 dB (continuous lines) for (a) power at OMS input of 0 dBm and (b) power at OMS input of 5 dBm. Optical preamplification in OMS is not considered in this case.30

36

36

32

32

OSNREST, dB

OSNREST, dB

Copyright © 2009, IEEE.

28 24 20 16 12

28 24 20 16 12

12

16

20 24 OSNRSIG, dB (a)

28

32

12

16

20 24 OSNRSIG, dB

28

32

(b)

FIGURE 6.16 Dependence of estimated OSNR on OSNRSIG for OSNRREF ¼ 28 dB (□), OSNRREF ¼ 22 dB (D), and OSNRREF ¼ 16 dB (○). Optical amplification or filtering in OMS is not considered in this case. The insets present eye diagrams of signal under analysis for OSNR values of 16 and 30 dB. (a) Power at OMS input of 0 dBm and (b) power at OMS input of 5 dBm.30 Copyright © 2009, IEEE.

The usage of a preamplified OMS allows the acquisition of valid asynchronous histograms with an average power at the OMS input as low as 25 dBm. Figures 6.17(a) and (b) present the dependence of the estimated OSNR, OSNREST, on the actual signal OSNR, OSNRSIG, assuming that the signal used to acquire the reference histogram presents an OSNR of OSNRREF ¼ 16, 22, and 28 dB, and considering average powers of 20 and 25 dBm at the input of the OMS, respectively. In the case of OSNRREF ¼ 28 dB, the OSNR estimates follow a behavior similar to the one observed for higher powers in Figures 6.16(a) and (b). Note that in the case of an average power at the OMS

36

36

32

32

OSNREST, dB

OSNREST, dB

6.3 Acquisition and processing of amplitude histograms

28 24 20 16 12 12

16

20 24 OSNRSIG, dB

(a)

28

32

163

28 24 20 16 12 12

16

20 24 OSNRSIG, dB

28

32

(b)

FIGURE 6.17 Dependence of estimated OSNR on OSNRSIG for OSNRREF ¼ 28 dB (□), OSNRREF ¼ 22 dB (D), and OSNRREF ¼ 16 dB (○). An EDFA is used within the OMS for pre-amplification. The insets present the eye diagram of the signal under analysis for OSNR values of 16 and 30 dB. (a) Power at OMS input of 20 dBm and (b) power at the OMS input of 25 dBm.30 Copyright © 2009, IEEE.

input of 25 dBm, OSNREST is independent of OSNRSIG, for values above 24 dB. This limitation is due to the noise generated by the digital oscilloscope and also due to the noise generated by the preamplifier, which masks the impact of ASE noise on the signal under analysis. However, it occurs for an average power at the OMS input 20 dB below the corresponding case without an optical preamplifier, demonstrating the advantage of the preamplified approach. When OSNRREF is reduced to 22 and 16 dB, we verify that the maximum measurable OSNR is limited to 24 and 26 dB, respectively, for errors below 1 dB and for both considered values of power at the OMS input. Note that in all considered cases, this technique required the adjustment of a single variable, the OSNR, to find a match between the estimated and measured histograms. It has been shown that the maximum measurable OSNR decreases with the power of the signal at the input of the OMS due to the influence of the noise generated in the OMS. However, this limitation may be overcome by lowering the value of the reference OSNR. The technique based on reference histograms may be applied, in principle, to arbitrary modulation formats.30 In the same reference, its application is demonstrated for 40-Gb/s NRZ signals with and without GVD-induced degradation, and also for 40-Gb/s RZ signals. In the latter case, the RZ signal is converted to a NRZ signal within the OMS by narrowband optical filtering.

6.3 GENERAL CONCEPTS ON THE ACQUISITION AND PROCESSING OF AMPLITUDE HISTOGRAMS 6.3.1 Sampling noise

Optical or electrical, most real-life signals have a continuous and analog nature. In order to process these signals using digital circuitry, analog-to-digital conversion is required. However, a digital signal is discrete in amplitude and time. To obtain an amplitude- and time-discrete signal from an

164


amplitude- and time-continuous signal a sampling process is required. The fundamental principal of sampling is the repeated quasi-instantaneous capture of a time-varying waveform by a sampling gate.31 A train of narrow impulses controls the sampling gate. After a sample of the continuoussensor signal is obtained, an ADC circuit can be used to obtain a corresponding digital value. The existing ADC circuits require a constant input for a certain amount of time in order to obtain a correct digital output. Therefore, a sample obtained with a narrow impulse is usually not enough for the ADC to function properly. In order to retain the impulse amplitude an analog memory is used between the sampling circuit and the ADC. The simplest analog memory is a capacitor, known as a hold capacitor. The charge accumulated when the gate is closed is proportional to the amplitude of the sample and can be used for analog-to-digital conversion. The combination of the sampling circuit with the hold capacitor is known as a sample-and-hold (S&H) circuit. An example of a simplified S&H block diagram is presented in Figure 6.18. When the sampling gate is closed, the input sensor signal current, iin(t), charges the hold capacitor. The time required for the sampling gate to open is known as the aperture time. After the gate is open, the capacitor discharges with a given fall-off rate, depending on the input impedance of the following circuitry. In most cases, a high input impedance amplifier is used to decrease the falloff rate. The time required for the hold capacitor to discharge should be sufficient for the ADC to produce a valid digital value for the acquired sample. Some of the main limitations of S&H systems are as follows: 1. Finite aperture time. The S&H takes a period of time to capture a sample of the sensor signal. Since the signal will vary during this time, the sampled signal can be slightly off. 2. Sampling jitter. Temporal variations of the sampling frequency lead to uncertainty of the sampling instant. This problem is particularly hazardous in synchronous sampling systems. 3. Signal feedthrough. Due to construction limitations of the sampling gate, some current “bleeds” through the switch to the capacitor causing the voltage to change slightly. 4. Signal droop. The voltage being held on the capacitor starts to slowly decrease over time due to the limited input resistance of the following circuitry. 5. Aperture uncertainty. Variations from sample to sample of the aperture time lead to additional sampling noise. For the characterization of sampling systems for the acquisition of asynchronous histograms, it may be considered that the input impedance of the ADC is high enough to assume a constant voltage at

Sampling gate

iin(t)

R

C Impulse generator

FIGURE 6.18 Simplified block diagram of S&H circuit.


165

the hold capacitor terminals. Additionally, currently existing fast ADCs allow the reduction of feedthrough. As so, limitations 3 and 4 will be neglected. Temporal variations of the aperture time (limitation 5) will also not be considered because the sampler characteristics are assumed perfect. The sampling jitter (limitation 2) leads to significant error in systems where the sampling frequency is related with the frequency of the sensor signal. These systems include all digital receivers, oscilloscopes, and time-domain reflectometry devices. However, when considering asynchronous sampling, the sampling frequency is not related to the frequency of the signal under analysis. Therefore, sampling jitter does not affect significantly the overall accuracy of the system. The aperture time (limitation 1) is the time required for the sampling gate to close after a sample is acquired. In an ideal situation, the aperture time is zero and the output signal of the S&H is proportional to the signal value immediately before the sampling instant. However, in a nonideal situation, the sampling gate requires a finite time to switch. During this time, the input signal of the S&H is changed, leading to sampling error. In order to evaluate the effects of finite aperture time in asynchronous sampling, one can assume the general model for an S&H system presented in Figure 6.19. The pulse generator creates a pulse train at the sampling frequency Fa with a pulse shape given by c(t). It is considered that c(t) is norR malized in a way that c(t) ¼ 1. The pulse train is multiplied by the signal under analysis, x(t), and the result is inserted in an integrator. This integrator is such that its output is a constant signal in each sampling period. The output value of the integrator for the kth sampling interval, xk, is given by þ1 ð

xk ¼

xðtÞ cðt k Ta Þ dt;

(6.17)

1

where Ta ¼ 1/Fa is the sampling period. In an ideal sampling system, the time required by the sampling gate to open is infinitesimal, with the sampling impulse given by c(t) ¼ d(t), where d(t) is the Dirac function. In such cases, we obtain xk ¼ x(kTa) and the sampling error is null. In a realistic case the sampling pulse is not infinitesimal and sampling error will occur. To address this error without loosing generality, we may consider the Taylor series expansion of x(t) around the sampling instant xðtÞ ¼

þ1 X ðt k Ta Þm m¼0

x(t )

m!

xðmÞ ðk Ta Þ;

∫ . dt +∞

Σ y (t − k . Ta)

k=−∞

FIGURE 6.19 General block diagram of S&H system.17


xk

(6.18)

166


where x(m)(t) is the mth order derivate of x(t). By replacing Equation (6.18) in Equation (6.17) we have xk ¼ xðk Ta Þ þ ek ;

(6.19)

where ek is the sampling error given by ek ¼

1 ðmÞ X x ðk Ta Þ m¼1

m!

cm ;

(6.20)

and the term cm is given by þ1 ð

cm ¼

tm cðtÞ dt:

(6.21)

1

Equation (6.19) shows that the sampling error depends mainly on the derivates of the sensor signal and the shape of the sampling function. Furthermore, if c(t) is an even function, the contribution of the even-order derivates of x(t) to the error is null.32 The overall sampling error can be considered as a form of noise and measured by its variance, s2SN ; " #2 Ns 1 X 1 X cm ðmÞ 2 sSN ¼ x ðk Ta Þ ; (6.22) Ns k¼1 m¼1 m! where Ns is the number of acquired samples. Assuming that the main contribution for the sampling error results from the first-order derivate and neglecting the contributions of the higher-order derivates, s2SN can be approximated by s2SN c21 s2x;1 ;

(6.23)

where s2x;m is the variance of the samples acquired from the mth order derivate of x(t), defined as s2x;m ¼

Ns 1 X ½xðmÞ ðk Ta Þ2 : Ns k¼1

(6.24)

In the particular case that even sampling impulses are used, the odd-order derivates of the sensor signal do not contribute to the error signal. As an approximation, only the contribution of the second-order derivate will be considered. The contribution of the higher-order derivates will be neglected. As so, s2SN can be approximated by s2SN

c22 2 sx;2 : 4

(6.25)

Equations (6.23) and (6.25) show that the sampling error depends essentially on the sampling impulse shape, and the variance of the first- or second-order derivates of the signal under analysis in the sampling instant.


167

The impact of the sampling noise may be characterized by a signal-to-sampling-noise ratio (SSNR), defined by Ps (6.26) SSNR ¼ 10 log10 2 ; sSN where Ps is the signal power. As an application example, Figures 6.20(a) and (b) present the SSNR calculated for an S&H with rectangular and triangular sampling pulses, respectively. In both cases, an aperture time ap is considered. It has been assumed that the signal under analysis is a binary NRZ signal with a raised-cosine transition function with transition time tr and symbol period T. Under these conditions, it may be shown that the sampling noise variance is approximated by s2SN

a4p p4 3 9216 T tr

(6.27)

when a rectangular sampling pulse is used. Similarly, when using a triangular sampling impulse, the sampling noise variance may be approximated by s2SN

a2p p2 : 144 T tr

(6.28)

Figure 6.20(a) or (b) shows that an increase of the transition time leads to a significant decrease of the SSNR. This decrease is more evident for low values of the aperture time. Comparing Figure 6.20(a) with Figure 6.20(b), the advantage of using a rectangular sampling impulse to a triangular sampling impulse becomes evident. The latter case presents a significant degradation of the SSNR due to the contribution of the sensor signal first-order derivate.

90

90 Sampling pulse ap

80

70

tr/T = 0.4

60

SSNR (dB)

SSNR (dB)

70

50 40 tr/T = 0.1

30

60 50

20 10 0.1 ap /T

(a)

1.0

tr/T = 0.4

30

10 0

tr/T = 0.1

40

20

0.0

Sampling pulse ap

80

0 0.0

0.1 ap/T

1.0

(b)

FIGURE 6.20 Signal-to-sampling-noise ratio as function of aperture time for different values of transition time. (a) Using rectangular sampling impulse. (b) Using triangular sampling impulse. Numerical simulation results, symbols; analytical results, continuous line.

168


Figure 6.20 also presents numerical simulation results to validate Equations (6.27) and (6.28). Note that the validity of this approach is limited to the validity of the Taylor series expansion of the signal under analysis around the sampling point. Since this signal is generally not an analytic function in the case of the considered optical systems, it may not agree with its Taylor expansion in all points. For digital signals, the critical points are those where the derivates of the signal are not continuous in the neighborhood of the symbol transitions. This justifies the decay of this approach for high sampling aperture times, as shown in Figure 6.20.

6.3.2 Averaging effects The computation of the sampling noise as a means to characterize an S&H system is effective when the signal under analysis is noiseless and one needs to address its shape. This is particularly significant for measurement instrumentation, such as digital oscilloscopes. However, in monitoring systems based on sampling the signal under analysis, such as the case of asynchronous histograms, it becomes important to address not only the signal but also the noise degrading this signal. As such, more effective methods to characterize the sampling system can be devised, in particular, methods focusing on analyzing the impact of the monitoring system on the signal under analysis.17 Consider the simplified block diagram for the equivalent S&H system, presented in Figure 6.21. In this model, the nonideal sampling system presented in Figure 6.19 is replaced by an ideal sampling system preceded by an equivalent sampling filter with an impulse response heq(t). In this system, the kth sample is given by ð þ1 xðtÞ heq ðk Ta tÞ dt: (6.29) xk ¼ xeq ðk Ta Þ ¼ 1

By comparing Equations (6.29) and (6.17), it can be shown that the two systems become equivalent by having heq(t) ¼ c(t). However, the system presented in Figure 6.21 clearly indicates that the performance quality measurements performed with the acquired samples evaluate xeq(t) instead of x(t). As such, this form of modeling allows treating the impact of nonideal sampling as an additional filter applied to the signal under analysis. Note that in reality such a filter does not exist, and for this reason one usually refers this phenomenon as averaging effect. To assess the impact of the sampling process on the evaluation of signals degraded by noise, consider the signal under analysis given by x(t) ¼ s(t) þ n(t), where s(t) and n(t) are signal and noise

x(t)

heq (t)

xeq (t )

ò . dt

xk

+⬁

Σ d (t − k . Ta ) k=1 FIGURE 6.21 General block diagram of equivalent S&H system.17



169

components, respectively. The ratio between the variance of the noise after the equivalent sampling filter, s2n;eq , and the variance of the monitored noise, s2n , is given by Ð þ1 2 s2n;eq 1 Sn ð f Þ jHeq ð f Þj df ; (6.30) ¼ Ð þ1 s2n 1 Sn ð f Þ df where Sn(f) is the power spectral density of the noise affecting the signal under analysis and Heq ð f Þ ¼ F½heq ðtÞ is the transfer function of the equivalent sampling filter, with F½ as the Fourier transform operator. Applying the Schwarz’s inequality to Equation (6.30) it can be shown that s2n;eq s2n . Therefore, if the distortion of the signal component is considered negligible, the sampling process may influence quality estimates by reducing the evaluated noise power. To illustrate the impact of averaging effects on optical monitoring systems based on the analysis of asynchronous histograms, consider the block diagram presented in Figure 6.22. The signal under analysis is initially filtered through an optical filter with a low-pass equivalent transfer function Ho( f ). After that, the signal is detected by a p-i-n photodetector with unitary responsivity. An electrical filter, with transfer function given by Hr( f ), models the frequency limitations of the receiver electronics. Finally, the filtered electrical current is sampled for histogram counting. The sampling system is assumed to have a structure equivalent to the system presented in Figure 6.21. The optical and electrical filter bandwidths are assumed to be large enough to neglect signal distortion due to filtering. In these conditions, the Q-factor, in linear units, can be approximated by33 m m0 q¼ 1 ; (6.31) s1 þ s0 where mm and sm are the values of the mean and standard deviation of the electrical current for the symbol m, with m ¼ 0 and 1 for the space and mark symbols, respectively. If the signal-spontaneous emission beat noise is considered dominant, the standard deviation of the electrical current for the symbol m can be approximated by34 ð þ1 s2m ¼ ksp ðPm Þ jHr ð f Þj2 jHo ð f Þj2 df ; (6.32) 1

where ksp(Pm) is a term that depends on the noise characteristics and the optical power at the receiver input for the symbol m, Pm. Due to the nonideal sampling process, the variance of the measured noise is given by ð þ1 2 sm;eq ¼ ksp ðPm Þ jHeq ð f Þj2 jHr ð f Þj2 jHo ð f Þj2 df : (6.33) 1

Signal under analysis 40-Gb/s NRZ

Nonideal S&H

ADC

FIGURE 6.22 Simulated nonideal histogram acquisition system for optical monitoring.

Histogram counter

170


Therefore, the measured Q-factor for the equivalent system becomes qeq ¼

m1 m0 q ¼ ; s1;eq þ s0;eq k

where the correction factor, k, is given by Ð þ1 jHeq ð f Þj2 jHr ð f Þj2 jHo ð f Þj2 df 2 : k ¼ 1 Ð þ1 2 2 1 jHr ð f Þj jHo ð f Þj df

(6.34)

(6.35)

Equation (6.34) shows that if the aperture time is short enough to neglect distortion of the signal under analysis (i.e., when the SSNR is high), one may consider that the impact of the nonideal sampling on quality measurements may be reduced to a proportional adjustment.17,35 The correction factor allows a simple and direct adjustment of the measured Q-factor in conditions where noise averaging due to the nonideal sampling process is dominant. This factor is only dependent on the physical characteristics of the measurement system and can be obtained by calibration of the device. To illustrate the application of the correction factor, consider the optical monitoring system based on the analysis of asynchronous histograms presented in Figure 6.22. In this system, the signal under analysis is simulated as a 40-Gb/s NRZ deBruijn sequence of 220 symbols. Finite sampling aperture time was considered by simulating rectangular sampling impulses. The OSNR of the signal under analysis is adjusted to provide simulated signals with Q-factors of q ¼ 8 and 10, in linear units. The OMS computes the Q-factor, qeq, without correction due to nonideal sampling using the asynchronous histogram analysis technique developed by Andre´ et al.,27 which is described in detail in Section 6.2.1. In addition, the corrected values of the Q-factor, qcor, have been calculated using the correction factor for this OMS. The dependence of qeq/q and qcor/q on the aperture time is presented in Figure 6.23, where each value was obtained by averaging the results from 16 simulations. In order to have a better understanding of Figure 6.23, it may be divided in three different regions. For sampling apertures below 20 ps, the averaging effect of nonideal sampling influences mainly the noise component. Therefore, qeq increases steadily with the aperture time due to the noise averaging. In this area, the correction factor can be applied, providing accurate values of the Q-factor. From sampling apertures of 2035 ps near the symbol period, the signal component also becomes significantly affected by the nonideal sampling, and the model used to obtain the estimated histogram becomes inaccurate. The distortion induced by the nonideal sampling in the signal component leads to new relative maximums in the simulated histogram, which are interpreted as noise by the fitting algorithm. To illustrate this effect, Figure 6.24 presents the asynchronous histograms obtained with an aperture time of 28 ps for a Q-factor of 8. The new relative maximums lead to an overestimation of the noise component, reducing the growth of the estimated value of qeq. As the correction factor continues to increase steadily with the aperture time, the values of qcor start to decrease. However, a margin of 25% of error is maintained. For aperture times above 35 ps the averaging of the signal and noise components due to nonideal sampling has degraded the shape of the histogram to a point where the histogram analysis technique fails completely. To illustrate this effect, Figure 6.24 also presents the asynchronous histograms obtained with an aperture time of 44 ps for a Q-factor of 8. In these conditions, the Q-factor estimates performed over the simulated histograms lose physical meaning.


171

3.5 3.0 qeq /q

2.5 2.0

qcor /q

1.5 1.0 0.5 0.0 0

5

10

15

20

25

30

35

40

Sampling aperture time (ps)

FIGURE 6.23 qeq /q and qcor /q as function of aperture time for 40-Gb/s signal. •, qeq /q for q ¼ 8; ○, qeq /q for q ¼ 10; n, qcor / q for q ¼ 8; □, qcor /q for q ¼ 10.17 Copyright © 2009, IEEE.

Normalized histogram

104

ap = 28 ps

103

ap = 44 ps 102 –0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Current level (mA)

FIGURE 6.24 Asynchronous histogram of a 40-Gb/s signal obtained with a nonideal sampling system from simulated signals with □, aperture time of 28 ps; •, aperture time of 44 ps. Continuous lines represent estimated histogram. Arrows indicate new relative maximums that result from averaging effect induced by nonideal sampling.17 Copyright © 2009, IEEE.

172


6.4 SUMMARY This work has shown that by using simple analysis techniques, such as the average Q-factor, one may employ asynchronous histograms to derive information that would otherwise require a complete optical receiver as well as a BER counter to estimate. More complex techniques, such as the decomposition of asynchronous histograms, allow handling signals that suffer from distortion to the point where the original signal format is unrecognizable. In addition, it has been shown that histogram comparison techniques either using theoretical histograms or reference histograms can be used to extract information on the signal under analysis. Some practical aspects on the design of asynchronous sampling systems for the acquisition of asynchronous histograms have been considered. In particular, the most demanding aspect has been shown to be the sampling aperture time. It has been shown that the aperture time of the sampling system conditions the performed measurement by restricting the noise or distorting the signal under analysis. As a consequence, compensation methods have to be used either by adjusting the performed measurement or by using reference histograms. In summary, the analysis of asynchronous histograms has survived the evolution of optical communications systems as an effective proposal for advanced optical performance monitoring thanks to its remarkable flexibility. Asynchronous histogram acquisition systems can be easily designed for very wide ranges of bit rates and arbitrary signal formats. An extensive set of simple and robust methods to analyze asynchronous histograms has been developed over the years, and application to novel modulation formats can be carried out with relatively little effort.

REFERENCES 1. Kilper D, Bach R, Blumenthal D, Einstein D, Landolsi T, Ostar L, et al. Optical performance monitoring. IEEE/OSA J Lightwave Technol 2004;22(1):294–304. 2. Hanik N, Gladisch A, Caspar C, Strebel B. Application of amplitude histograms to monitor performance of optical channels. IEE Electron Lett 1999;35(5):403–4. 3. Shake I, Takara H, Kawanishi S, Yamabayashi Y. Optical signal quality monitoring method based on optical sampling. IEE Electron Lett 1998;32(22):2152–4. 4. Shake I, Takara H, Uchiyama K, Yamabayashi Y. Quality monitoring of optical signals influenced by chromatic dispersion in a transmission fiber using averaged Q-factor evaluation. IEEE Photon Technol Lett 2001;13(4):385–7. 5. Shake I, Takara H. Averaged Q-factor method using amplitude histogram evaluation for transparent monitoring of optical signal-to-noise ratio degradation in optical transmission system. IEEE/OSA J Lightwave Technol 2002;20(8):1367–73. 6. Shake I, Otani E, Takara H, Uchiyama K, Yamabayashi Y, Morioka T. Bit rate flexible quality monitoring of 10 to 160 Gbit/s optical signals based on optical sampling technique. IEE Electron Lett 2000;36(25): 2087–8. 7. Shake I, Takara H, Kawanishi S. Simple Q factor monitoring for BER estimation using opened eye diagrams captured by high-speed asynchronous electroopical sampling. IEEE Photon Technol Lett 2003;15(4):620–2. 8. Shake I, Takara H. Chromatic dispersion dependence of asynchronous amplitude histogram evaluation of NRZ signal. IEEE/OSA J Lightwave Technol 2003;21(10):2154–61.

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9. Shake I, Takara H, Morioka T. Determination of the origin of BER degradation utilizing asynchronous amplitude histograms. In: Proc. Pacific Rim conference on lasers and electro-optics, 2001 (CLEO/Pacific 2001), Chiba, Japan, vol. 2; p. II-560–II-561. 10. Rasztovits-Wiech M, Studer K, Leeb W. Bit error probability estimation algorithm for signal supervision in all-optical networks. IEE Electron Lett 1999;35(20):1754–5. 11. Weinert C, Schmidt C, Weber H. Application of asynchronous amplitude histograms for performance monitoring of RZ signals. In: Proc. optical fiber communication conference 2001 (OFC 2001), Anaheim, California, USA, vol. 3; 2001. p. WDD41-1–WDD41-3. 12. Andre´ P, Pinto J, Teixeira A, Rocha J, Almeida T, Pousa M. Optical-signal-quality monitor for bit-errorratio assessment in transparent DWDM networks based on asynchronously sampled amplitude histograms. OSA J Opt Network 2002;1(3):118–28. 13. Andre´ P, Pinto J. Bit error rate assessment in DWDM transparent networks using optical performance monitor based in asynchronous sampling. In: Proc. optical fiber communication conference 2002 (OFC 2002); Anaheim, California, USA, 2002. p. 749–50. 14. Teixeira A, Andre´ P, Lima M, da Rocha J, Pinto J. Characterization of high bit rate optical signals by low rate asynchronous sampling. In: Proc. lasers and electro-optics society 2002 (LEOS 2002), Glasgow, Scotland, UK, vol. 2; 2002. p. 625–6. 15. Ye D, Zhong W. Improved BER monitoring based on amplitude histogram and multi-Gaussian curve fitting. OSA J Opt Network 2007;6(6):584–98. 16. Ding L, Zhong W, Lu C, Wang Y. New bit-error-rate monitoring technique based on histograms and curve fitting. OSA Opt Expr 2004;12(11):2507–11. 17. Luı´s R, Andre´ P, Teixeira A, Monteiro P. Performance monitoring in optical networks using asynchronously acquired samples with nonideal sampling systems and intersymbol interference. IEEE/OSA J Lightwave Technol 2004;22(11):2452–9. 18. Chen H, Poon A, Cao X. Amplitude histogram-based method for inline pulse rise time monitoring without eye-diagram. In: Proc. conference on lasers and electro-optics 2003 (CLEO 2003), Washington, D.C., USA, 2003. p. 1511–3. 19. Chen H, Poon A, Cao X. Transparent monitoring of rise time using asynchronous amplitude histograms in optical transmission systems. IEEE/OSA J Lightwave Technol 2004;22(7):1661–7. 20. Weinert C, Caspar C, Konitzer M, Rohde M. Histogram method for identification and evaluation of crosstalk. In: Proc. optical fiber communication conference 2000 (OFC 2000), Baltimore, Maryland, USA, vol. 3; 2000. p. 56–8. 21. Weinert C, Caspar C, Konitzer M, Rohde M. Histogram method for identification and evaluation of crosstalk. IEE Electron Lett 2000;36(6):558–9. 22. Benlachtar Y, Killey R, Bayvel P. Identification of sources of degradation in optical channels using deconvolution technique. In: Proc. optical fiber communications conference 2003 (OFC 2003), Atlanta, Georgia, USA, vol. 1; March 2003. p. 109–10. 23. Weinert C. Gaussian deconvolution method for identification of impairments in optical signal transmission. OSA J Opt Network 2004;3(6):361–87. 24. Luı´s R, Teixeira A, Andre´ P, Monteiro P. Evaluation of intra-band crosstalk using asynchronous histograms. In: Proc. European conference on networks and optical communications 2004 (NOC 2004), Eindhoven, The Netherlands, 2004. p. 374–81. 25. Noirie L, Ce´rou F, Moustakides G, Audouin O, Peloso P. New transparent optical monitoring of the eye and BER using asynchronous under-sampling of the signal. In: Proc. European conference on optical communication 2002 (ECOC 2002), Copenhagen, Denmark, vol. 5; 2002. p. 1–2.

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26. Mobilon E, Barros M, Lopes A. Experimental verification of an eye diagram reconstruction technique based on asynchronous undersampling. In: Proc. international conference on microwave and optoelectronics 2005; Brası´lia, Brazil, 2005. p. 603–6. 27. Andre´ P. Optoelectronic components for high bitrate photonic networks. Ph.D. Thesis, Department of Electronics and Telecommunications Engineering, University of Aveiro, Aveiro, Portugal, 2002. 28. Kilper D, Weingartner W. Monitoring optical network performance degradation due to amplifier noise. IEEE/OSA J Lightwave Technol 2003;21(5):1171–8. 29. Yang W. Sensitivity issues of optical performance monitoring. IEEE Photon Technol Lett 2002;14(1): 107–9. 30. Luı´s R, Teixeira A, Monteiro P. Optical signal-to-noise ratio estimation using reference asynchronous histograms. IEEE/OSA J Lightwave Technol 2009;27(6):731–43. 31. Kahrs M. 50 Years of RF and microwave sampling. IEEE Trans Microw Theory Tech 2003;51(6): 1787–805. 32. Sauerwald M. Effects of aperture time and jitter in a sampled data system. In: National semiconductor application note AD-03. Available at: http://www.national.com. 1994. 33. Bergano N, Kerfoot F, Davidson C. Margin measurements in optical amplifier systems. IEEE Photon Technol Lett 1993;5(3):304–6. 34. Rebola J, Cartaxo A. Power penalty assessment in optically preamplified receivers with arbitrary optical filtering and signal-dependent noise dominance. IEEE/OSA J Lightwave Technol 2002;20(3):401–8. 35. Li Z, Li G. Chromatic dispersion and polarization-mode dispersion monitoring for RZ-DPSK signals based on asynchronous amplitude-histogram evaluation. IEEE/OSA J Lightwave Technol 2006;24(7):2859–66.

CHAPTER

Optical performance monitoring based on asynchronous delay-tap sampling

7

Trevor B. Anderson*,{, Sarah D. Dods*, Adam Kowalczyk*,{, Ken Clarke*, Don Hewitt{, and Jonathan C. Li* *

Monitoring Division Inc., Melbourne, Australia National ICT Australia Ltd. (NICTA), Victoria Research Laboratory, Australia

{

7.1 INTRODUCTION Reconfigurable and self-managed optical networks offer the potential for significant operational savings through automated fault diagnosis and management, path provisioning, and optimization.1,2 However, realizing this potential requires real-time monitoring of optical impairments and their distribution within the network. In addition to OSNR, these impairments include PMD, four-wave mixing, chromatic dispersion, reflections, and both baseband and in-band crosstalk. Future networks will also potentially carry a wide variety of formats and data rates. To accommodate these requirements, optical performance monitors will need to be ubiquitously distributed throughout the network, compatible with multiple formats and capable of monitoring multiple simultaneous impairments. We have recently introduced a new monitoring technique known as asynchronous delay-tap sampling that promises to satisfy the above requirements. With this technique, multiple impairment measurements as well as signal quality are extracted from a two-dimensional histogram of the signal.3,4 This histogram, known as a phase portrait, provides the information richness of an eye diagram without the requirement of clock extraction. The phase portraits contain unique impairment signatures that can be discovered using statistical pattern recognition techniques. With this approach we are able to not just classify, but to quantitatively monitor simultaneous combinations of impairments. A key advantage of the technique is that a simple direct detection receiver can be used to monitor a variety of bit rates and modulation formats without the need for demodulation of the signal or modification of the receiver bandwidth. The monitoring of a new impairment or format is enabled by downloading the relevant pattern recognition algorithm to the monitor. Many alternative techniques have been proposed to measure different subsets of impairments. These techniques can be broadly classed as either spectral5 or sampling based. The former include the use of RF tones and measurement of RF clock power. These techniques are, however, format dependent. The laboratory eye diagram is the most familiar sampling technique for measuring signal quality and estimating the underlying causes,6 but requires tunable clock recovery and can be difficult to extract for strongly distorted signals. The asynchronous histogram technique has been proposed as an alternative sampling technique that does not require clock recovery,7,8 but while sensitive to © 2010 Elsevier Inc. All rights reserved. Doi: 10.1016/B978-0-12-374950-5.00007-9

175

176

CHAPTER 7 OPM based on asynchronous delay-tap sampling

multiple impairments, unique identification of a particular impairment in the presence of other impairments remains a challenge. Monitoring of digital coherent systems using the information available in the digital equalizer at the receiver has recently been demonstrated for CD, PMD and OSNR, and PDL.9,10 While these techniques provide a cost-effective solution at the receiver, the real-time sampling requirements mean that they cannot be readily used at intermediate points in the network. In contrast, the potential for a distributed solution based on a simple direct detection receiver is a key attribute of the asynchronous delay-tap technique. Early simulations of the asynchronous delay-tap technique for 10-Gb/s NRZ showed the potential for simultaneous monitoring of OSNR, CD, DGD, and crosstalk, as well the ability to measure signal quality parameters Q and timing jitter.3 The first experimental results using pattern recognition (with a training set of 300 cases) were demonstrated for simultaneous combinations of OSNR, CD, and DGD.4 An important limitation of these earlier works was that the signal polarization was such that first-order PMD had a fixed and equal power split between principal states. In recent work,11 the polarization restriction has been removed, with the first demonstration of CD and first-order PMD with random power splits on a commercial 10-Gb/s NRZ WDM test channel. This work used an automated network emulator that enabled larger training sets (900 cases) to be created. More recently the technique has been demonstrated in simulations and experiments for a variety of formats including 40-Gb/s NRZ-DPSK, 10-Gb/s RZ-DPSK, and 20-Gb/s RZ-DQPSK.12,13 We note that while the ability to monitor phase-modulated formats with a simple direct detection receiver is a key benefit of the technique, the method is also compatible with coherent-based receivers.14 In this chapter, we review the technique beginning with a discussion and interpretation of the phase portrait in Section 7.2, and a brief overview of pattern recognition approaches. Experimental demonstrations of OSNR, CD, and DGD monitoring for NRZ and DPSK formats are described in Section 7.3, where we also introduce a measure of DGD that takes into account arbitrary power splits between principal polarization states. Early proof of principle demonstrations, based on single transponder and laboratory-generated impairments, is further validated at 10-Gb/s with WDM system measurements and training over combined transponders. To make the technique more robust, the training set can be extended to include other effects that may potentially induce errors, such as alternative sources of noise, filter drift, or delay variations. In Section 7.4, the ability of the technique to accommodate optical filter-induced distortions and crosstalk is demonstrated for 10-Gb/s NRZ. Finally, the format independence of the technique is demonstrated with simulations of CD and DGD monitoring for 40-Gb/s RZ-DQPSK using the same monitoring hardware employed in 10-Gb/s NRZ. A summary of the techniques covered in this chapter is shown in Table 7.1.

7.2 TECHNIQUE The two key components to the technique that we describe in this section are the construction of the phase portraits followed by the application of statistical pattern recognition techniques to extract impairment signatures.

7.2 Technique

177

Table 7.1 Summary of Asynchronous delay-tap sampling Techniques Technique

Formats Demonstrated

Impairments Demonstrated (range)

Asynchronous sampling AI—support vector machine3,4,11,23,24

10G NRZ 40G NRZ-DPSK 40G RZ-DQPSK* 80G PMDQPSK*

OSNR (10, 30) dB CD (1400, 1400) ps/nm PMD (0, 60) ps In-band crosstalk* (15–25) dB Filter offset* (12,12) GHz OSNR (8.7, 35) dB CD (600, 600) ps/nm PMD OSNR (10, 30) dB CD (0, 800) ps/nm CD* (0,400) ps/nm

Asynchronous sampling—Hough transformation8,12,13 Asynchronous sampling—Homodyne detection14 Asynchronous sampling—Hausdorff measure17 Asynchronous sampling AI—artificial neural networks18

10G 20G 40G 10G

RZ-DPSK RZ-DQPSK* NRZ-DPSK NRZ-DPSK

40G NRZ-DPSK* 10G NRZ*

OSNR* (15, 30) dB CD* (0, 55) ps/nm PMD* (0, 10) ps

*Indicates simulation results.

7.2.1 Phase portrait Asynchronous delay-tap sampling is an alternative to the eye diagram that uses the joint probability density function (pdf) of a signal x(t), and its delayed version x(t þ Dt) to characterize the signal.1 This pdf, known as a phase portrait, is sensitive to waveform distortion and noise and contains unique signatures of impairments. To generate the phase portrait, the waveform is sampled in pairs separated by a known delay Dt, as shown for the NRZ signal in Figure 7.1. The phase portrait is then created by binning the sample pairs into a two-dimensional histogram, as shown in Figure 7.2(a) and (b) for 1-bit and ¼-bit delays, respectively. We emphasize that the sampling is asynchronous, in that the time between the pairs, Ts, is not related to the monitored signal bit rate, and can be many orders of magnitude longer with thousands of bits between sample pairs. A fundamental difference between the eye diagram and phase portrait is that the latter contains information about the

1

Unbeknown to the authors in the early development of this monitoring technique, delay-tap sampling is used in the field of nonlinear time series analysis16 where it is referred to as the method of “time-delay embedding.” It is also where the term phase portrait originates and in this context the word “phase” has no relation to optical phase. (The space of delayed variables can be shown through Taken’s theorem to be isomorphically related to the phase space of the underlying system parameters). The technique has been applied to problems as diverse as the analysis of electrocardiograms. The optimal choice of delay is the subject of numerous papers; a common heuristic is to use a delay equal to the correlation time of the signal.

178


Asynchronous clock From monitor port

ADC x Δt

ADC y

FIGURE 7.1 Schematic of asynchronous delay-tap sampling technique. Sample pairs are separated by a fixed delay, Dt.

(1,1)

x

(1,0)

Δt = ¼T

y

Δt = T

y

(0,1)

101 and 010 transitions (0,0)

(a)

x

(b)

FIGURE 7.2 NRZ phase portraits for (a) 1-bit delay, and (b) ¼-bit delay.

correlation between samples on the time scale of a bit period. This information is absent in eye diagrams that are constructed from samples that are separated by long periods. To help interpret the 1-bit delay-phase portrait, we begin by noting that the corners of the phase portrait represent sample pairs of well-defined marks and spaces. The lines joining these points originate from samples taken during the bit transitions between these states. For example, the diagonal line represents the 010 and 101 transition. Because the sampling is asynchronous, the waveform is sampled uniformly in time and therefore the density of points in these lines is inversely proportional to the slope of the waveform. For an idealized rectangular waveform, there would only be the four corner points; in contrast, a triangular waveform would give a uniform distribution of points along the lines. As we will show in the following, waveform distortions in the time-domain manifest as variations in both the curvature and density of these lines. In Figure 7.3 we show the effects of changing the tap delay. For small offsets from the 1-bit delay, we see that the diagonal line splits into separate curves for the 101 and 010 transitions. The splitting is a useful means of comparing the alignment of the bit rate and delay. In another application, the increased splitting or distortion in the phase portrait has been proposed as a method for aligning the pulse carver and data modulator in RZ-OOK modulators.15 We note that while the choice of 1-bit delay Dt is a useful tool for visualization, it is not critical for pattern recognition. (This enables a fixed delay of 25 ps, for example, to be used for both 10- and

7.2 Technique

Δt = T(1 − 1/16)

Δt = T

179

Δt = T(1 − 1/16)

FIGURE 7.3 Phase portraits of an NRZ signal showing the effects of small changes in tap delay.

40-Gb/s signals.) For short time delays, such as ¼ T, we can interpret the phase portrait as approximating the joint probability density of the amplitude and slope of the waveform with respect to time. The width of this phase portrait, as measured along its minor axis, can be interpreted as an approximate measure of the maximum slope of the waveform. The curves above and below the line y ¼ x represent the positive and negative slopes of the waveform, respectively. An asymmetry about this line therefore represents an asymmetry in rising and falling transients. In addition to impairment monitoring, the ¼-bit phase portraits can also be used, in an analogous manner to the eye diagram, to measure signal quality. For example, the standard signal quality measure Q can be approximated from the distribution of points along the lines y ¼ x corresponding to sample pairs of zero slope. In a more sophisticated approach, we could apply pattern recognition techniques to compensate for chromatic dispersion and monitor Q midspan as it would be measured at the receiver postcompensation. To help understand the effects of different impairments on the phase portrait, we have simulated OSNR, CD, PMD, and in-band crosstalk on a 10-Gb/s NRZ signal. The resulting phase portraits and eye diagrams are shown in Figure 7.4. Figure 7.4(a) shows the results for no optical impairment ASE noise

Dispersion

PMD

Crosstalk

All

(a)

(b)

(c)

(d)

(e)

(f)

T/4

Delay = T

Eye

Clean

FIGURE 7.4 Eye diagrams and phase portraits (1-bit delay, ¼-bit delay) for 10-Gb/s NRZ: (a) OSNR ¼ 35 dB and no impairments; (b) OSNR ¼ 25 dB; (c) CD ¼ 800 ps/nm; (d) DGD ¼ 40 ps; (e) crosstalk ¼ 25 dB; and (f) OSNR ¼ 25 dB, CD ¼ 800 ps/nm, PMD ¼ 40 ps, and crosstalk ¼ 25 dB.

180


(OSNR 35 dB), with a clean eye, and with a well-defined geometric shape in the phase portrait. Figure 7.4(b) shows the effects of reducing the OSNR to 25 dB. The predominant effect is to broaden the high-power regions of both the eye and phase portrait, but the underlying geometric shapes are not affected. Figure 7.4(c) shows OSNR of 35 dB with 800 ps/nm of CD. The eye diagram shows the characteristic narrowing of the peaks and reduction in the amplitude of the 010 transition as measured at a 1-bit period in width. In the phase portrait, this manifests as the diagonal curving in toward the origin (the intersection of the diagonal and the line y ¼ x represents the height of the 010 pulse as measured at 1-bit period width). Figure 7.4(d) shows the effects of 30 ps of first-order PMD, with the power split equally between the principal polarization states. The eye shows the characteristic “triangularization,” but still looks very clean. Interestingly, at first glance, the phase portrait does not show the effects of PMD. However, a closer inspection shows significant differences in the distribution of points along the lines. Figure 7.4(e) shows the phase portrait with a single source of in-band crosstalk at 25 dB. Both the eye and two-tap plots show similar broadening to the OSNR degradation, but different noise statistics. Finally, Figure 7.4(f) shows OSNR of 25 dB with the combined degradations from Figures 7.4(a)–(e). These initial results suggested that the phase portraits contained impairment signatures that could be exploited to enable us to distinguish between varieties of optical impairments. As we will show in the next section, this is indeed the case; in particular, there exist features that enable us to identify and measure individual impairments even in the cases where they occur simultaneously, as in Figure 7.4(f). However, such a determination of individual impairment values for mixed impairments requires more sophisticated approaches than handcrafted measurement of image features. For instance, the curvature of the diagonal line in the 1-bit phase portrait has been proposed as a measure of CD; however, it is obscured by ASE noise and reduced by PMD. To address this issue in a principled manner, we used statistical learning techniques to automate feature selection. As emphasized in the introduction, the technique can monitor both amplitude and phasemodulated formats without the need for expensive demodulation. For example, Figures 7.6(d)–(f) and Figures 7.11(a)–(c) show sample phase portraits (25-ps delay) for 40-Gb/s NRZ-DPSK and 40-Gbs/s RZ-DQPSK signals, respectively. In the former case, the NRZ-like structure is due to the partial demodulation of the signal resulting from the tuneable channel selection filter. In general, the amplitude variation and structure of the phase portraits for these formats derives from a combination of the residual amplitude variation in the format, phase-to-amplitude conversion from impairments (i.e., CD), and finally, phase-to-amplitude conversion due to the channel selection filter.

7.2.2 Pattern recognition Phase portraits can be treated as images and lend themselves well to a variety of image analysis and pattern recognition techniques. These techniques, which we briefly describe next, include Hough transforms,12 Hausdorff measures,17 artificial neural networks,18 Kernel methods, and support vector machines.19,20 In the first example, a ¼-bit phase portrait was used to monitor a 10-Gb/s RZ-DPSK signal.12 For this case, a Q estimate (using the distribution of points along the major axis of the phase portrait) was used to monitor OSNR, while the width of the phase portrait was used to monitor CD. In practice, the width is obtained by using an image processing technique known as a Hough transformation.

7.2 Technique

181

Although this technique was demonstrated for simultaneous OSNR and CD monitoring, the presence of cross-coupling between the impairments limited the sensitivity of the technique. A simulation of CD monitoring in the presence of OSNR and PMD was demonstrated for 40-Gb/s NRZ-DPSK using a shape comparison method known as Hausdorff distance in Reference 17. The demonstration was, however, limited to CD monitoring with OSNR robustness above 22 dB. An alternative model that overcomes the coupling between impairments18 uses artificial neural networks (ANNs) to map phase portrait features onto the three impairments—OSNR, CD, and DGD. The features used to describe the phase portrait, denoted by ( r 1 , s1, r3 , s3, x2 , y2 , Q31), are obtained by dividing the portrait into quadrants, as shown on the left in Figure 7.5. Here r and s represent the expected value and standard deviation of the radial coordinate in the first and third quadrants, respectively, with x2 and y2 representing the mean values of the first and second samples in the second quadrant. The fourth quadrant was not used, as it was assumed to be symmetric with respect to the second. The final parameter is similar to the approximate Q-factor described above; it is defined as Q31 ¼ ðr3 r1 Þ=ðs1 þ s3 Þ. The ANN learns the relationships between the input features and the output impairments. The ANN architecture used had a three-layer structure, consisting of an input, hidden, and output layers. Using this approach, a correlation coefficient between input and output predictions of 0.97 was obtained for a simulated 10-Gb/s NRZ system with relatively few (140) training cases. Although strongly correlated, it is noted that the CD predictions show significant offsets at low CDs for OSNRs at the lower end of the training range (18 dB). An important limitation of this result was the restriction to worst-case DGD with equal power splits in the principle states. (For a general PMD power split ratio, g, the phase portrait is not symmetrical about the y ¼ x axis.) While the features used in the above approaches have the advantages of being interpretable, they are applicable for only a restricted range of formats and impairments. For this reason, the approach used by the authors is to automate the feature selection. The first step in the training process is to bin the sample pairs into a two-dimensional histogram. For the work presented here, a 3030 histogram was used and all 900 features (representing the number of hits in each bin) are available for training.

PC

DGD

TDCM

RF splitter

γ

Tx_1

OSNR Tx_i

TOF

AS

20 GHz

VOA

VOA Network emulator

TOF 30 GHz

Variable delay

Asynchronous multi-impairment monitor

FIGURE 7.5 Setup for generation of training sets. The impairment emulator adds known combinations of OSNR, CD, and first-order PMD to clean transponder signals. A polarization controller ensures a random distribution of power splits between principal states. AS, asynchronous sampler and outboard processing; PC, polarization controller; TDCM, tunable dispersion compensation module; TOF, tunable optical filter; Tx, transponder under test; VOA, variable optical attenuator.

182


A key advantage of this approach is that it can be used for any format or tap delay without the need for fine-tuning of the algorithm. In our approach, we use kernel-based techniques to model the impairments. The predictor for each impairment can, in general, be assumed to be a weighted nonlinear combination of features. For example, the predictor for absCD can be written as jCDj ¼

n X

a j k xj ; x ;

j¼1

P where k() is in general a nonlinear kernel function jCDji ¼ nj¼1 aj k xj ; xi of the phase portrait feature vector x to be measured and the n phase portraits in the training set. The training process determines the weights aj, which jointly minimizes the error function, and a regularization term, the latter of which is used to avoid over-fitting.20 In practice, the training sets are created using a “network emulator” to add known quantities of the impairments to a clean signal. Training is done independently for each of the impairments by minimizing the impairment prediction error (e.g., CD) in the presence of the other background impairments (e.g., OSNR and DGD). This enables the CD predictor to be valid over the DGD and OSNR range included in training. For impairment levels outside this range, the CD will be expected to suffer from OSNR and DGD dependence. We point out that the CD prediction does not require simultaneous DGD or OSNR predictions to be made, as the predictors are independently trained. Finally, we note that the pattern recognition techniques described here can also be applied to the analysis of eye diagrams. In particular, we note the use of support vector machines6 and ANNs. An experimental comparison of monitoring with eye diagrams and phase portraits was shown in Reference 4, in which it was found that the phase portrait outperformed eye diagrams.

7.3 EXPERIMENT We demonstrated the technique on 10-Gb/s NRZ and 40-Gb/s DPSK signals with simultaneous OSNR, CD, and DGD with random polarization. Figure 7.5 shows a schematic of the experimental setup used for training and testing. Signals for training can be sourced directly from transponders, as in this demonstration, or alternatively from a tap point at the start of the link. For this demonstration, the 10-Gb/s signals were generated with commercial transponders, while the 40-Gb/s signal was generated with a tunable DFB modulated with a dual-drive Mach Zehnder. In both cases, 223 1 PRBS was used. The clean signals were passed to a custom network emulation unit with known combinations of impairments for the training phase. The signal was then input to the delay-tap multiimpairment monitor to create and analyze the phase portraits. Control of the network emulator and the data processing of the multi-impairment monitor were done by an external laptop and an on-board processor, respectively.

7.3.1 Network emulator In the network emulator, the signal was amplified with an EDFA to overcome emulator loss, and filtered with a tunable 30-GHz (@ 3 dB) filter. A polarization controller was used to randomly select a polarization state and ensure a random distribution of g. Various known combinations of DGD and

7.3 Experiment

183

CD were then added to the monitored signal. The DGD emulator had a range of 0 to 100 ps, and g ¼ Ifast/(Ifast þ Islow) was derived from DC optical power measurements (averaged over the sampling time) using taps in the fast and slow axes of the DGD emulator. The polarization controller was programmed to step through random polarization states and was held constant during the sampling of each phase portrait. The OSNR was controlled by coupling in a variable amount of ASE, generated by filtering and amplifying the output of an EDFA. Varying levels of OSNR were used to ensure that the CD and PMD predictions were valid across normal operational ranges. The ASE source was placed after the PMD emulator to ensure that the measurement of g was not compromised by the added ASE noise. CD was controlled using a tunable dispersion compensation module with a range of 1400 to 1400 ps/nm. The impaired signal was then passed to the MIM for sampling and analysis.

7.3.2 Multi-impairment monitor In the monitor, the signal was amplified to deliver a constant power of 0 dBm to the photodiode, after then filtering with a tunable optical filter with a 3-dB bandwidth of 30 GHz. The signal was then fed to a 20-GHz receiver followed by a 50:50 splitter with tunable electrical delay in one arm. The signal and delayed ports were asynchronously sampled at 40 kS/s using a customized dual-channel A/D. For every network emulator setting, a phase portrait was generated from 40,000 sample pairs. Training and test sets were created by programming the emulator to step through combinations of CD from 1400 to 1400 ps/nm (200-ps/nm steps) and DGD from 0 to 22.5 ps (2.5 ps steps), while varying the OSNR between 15 and 25 dB (eight levels), giving a total of approximately 2000 phase portraits. We note that the polarization state, and hence g, varied randomly across this phase portrait data set. In practice, allowing for emulator tuning setup times, the training time is approximately 4 h.

7.3.3 First-order PMD In our first demonstration of the technique,3,4 the impairments included were OSNR, CD, and DGD, where the latter was restricted to worst-case polarization alignment of the signal and the principal states. More recently, we have demonstrated the technique at 10-Gb/s taking into account both the differential group delay (DGD) and a random power split g between principal states. To do this, we define an effective differential group delay, DGDeff ¼ 4g(1-g)DGD, that is proportional to first-order string length and is a measure of first-order PMD system penalty.21,22 When the signal is aligned with either principal state of polarization (PSP) (g ¼ 0 or 1), DGDeff ¼ 0 and the signal remains undistorted regardless of the level of first-order PMD. The factor of 4 in DGDeff is chosen so that when g ¼ ½, DGDeff ¼ DGD. The advantages of using DGDeff are that it is directly related to the induced signal distortion, and thus the first-order PMD-induced system penalty, and it provides a dynamic measure of feedback for PMD compensation.

7.3.4 Results for 10-G NRZ and 40-G NRZ-DPSK Figure 7.6 shows a selection of phase portraits taken from the training sets. For the 10-Gb/s signals, we noted characteristic narrowing of the phase portrait and more uniform width due to DGD. A closer inspection shows that the phase portrait is not perfectly symmetric; this can be partly


DGDeff: 0.4 ps, absCD: 0



(a)

(b)

(c)




(d)

(e)

(f)

40-Gb/s DPSK

10-Gb/s NRZ

184

FIGURE 7.6 Experimental phase portraits for (a–c) 10-Gb/s NRZ and (d–f) 40-Gb/s DPSK showing the effects of CD and DGD; the OSNR is 14 dB for all cases. The tap delay was set at 25 ps for both bit rates. The impairment levels were DGDeff (ps), absCD (ps/nm): (a) 0,0, (b) 39,0, (c) 0,1200, (d) 0,0, (e) 14,0, and (f) 2,400.

attributed to g not equal to 1. The optical filter bandwidth of 30 GHz partially demodulates the 40-G DPSK signal resulting in the characteristic NRZ portrait. The effects of CD are clearly seen in the loop structure of Figure 7.6(f) and, as with 10-Gb/s NRZ, there is a reduction in the width of the portrait with increasing DGD. For each format, a set of 1500 phase portraits generated by the emulator was used to generate independent prediction models for OSNR, CD, and DGDeff. Tests were carried out on a further 250 randomly selected impairment combinations that were not included in the training set. Test results are shown, using an average of five measurements for each impairment combination, in the ordered plots of Figure 7.7. For 10 G, we find that RMS error at the 2s level for OSNR is 0.2 dB, and jCDj is 55 ps/nm over a range of 1400 ps/nm to 1400 ps/nm and 3.6 ps for DGDeff over 0–60 ps. For 40-Gb/s DPSK, the result for OSNR is 0.7 dB, and jCDj is 9 ps/nm over a range of 400 ps/nm to 400 ps/nm and 1.5 ps for DGDeff over 022 ps. A breakdown of the 10-G results shows that the accuracy of the DGDeff degrades, as expected, with poor OSNR and high CD. For 40-G DPSK, however, it was found that the DGDeff accuracy degrades with poor OSNR, but surprisingly improves with increasing CD. A possible interpretation of this is that the CD-induced, phase-to-amplitude conversion provides a larger waveform (Figure 7.6(f)) for DGDeff to distort.

7.3 Experiment

OSNR (dB), stdTe = 0.22 17

absCD (ps/nm), stdTe = 55.0 1500

10-Gb/s NRZ

DGDeff (ps), stdTe = 3.6 60 50

16 1000

15

40 30

14

500

20

13 12

10 0

100

200

300

0

0

100

(a) OSNR (dB), stdTe = 0.69

25

20

15

0

100

200

300

0

0

100

(b)

30

40-Gb/s DPSK

185

200 (d)

300

absCD (ps/nm), stdTe = 9.1 500

25

400

20

300

15

200

10

100

5

0

0

100

200 (e)

200

300

(c)

300

0

DGDeff (ps), stdTe = 1.5

0

100

200

300

(f)

FIGURE 7.7 Experimental measurements (gray) of simultaneous OSNR, CD, and DGD impairments for (a–c) 10-Gb/s NRZ and (d–f) 40-Gb/s NRZ-DPSK. Results for 250 test cases are ordered along the x axis by true values (shown in black). The test errors, stdTe, are quoted at the 2s level.

The previous results can only be regarded as proof of principle since they have used training and test sets generated from the same transponder and impairment emulator. Tests for transponder dependency were performed on three commercial 10-G transponders. The results for different combinations of training sets are shown in Table 7.2. We found that training and testing on separate transponders can give rise to significant errors. Training sets from combined transponders, however, add robustness to the measurements. Verification of the impairment emulator is shown in test results on independent impairments in Table 7.3. In this case, the CD for test cases is generated from varying lengths of SMF fiber and the PMD from birefringent fiber. For further verification, the technique has been recently demonstrated on a 10-Gb/s NRZ WDM system test bed that includes ROADMs.23

7.3.5 System testing Measurements of CD and OSNR were performed on an 800-km, 10-Gb/s NRZ test bed (50-GHz channel spacing) using three monitoring points, as illustrated in Figure 7.8. At each point, the CD

186


Table 7.2 Test Errors for Transponder T2 Showing Effects of Including Combinations of Transponders in Training Training Set

OSNR (dB)

TX3 TX1TX3 TX1TX2TX3 TX2

absCD (ps/nm)

0.6 0.7 0.5 0.4

DGDeff (ps)

51 45 40 26

2.8 2.6 2.5 2.1

Table 7.3 Independent Validation of Impairment Emulator OSNR (dB)

absCD (ps/nm)

DGDeff (ps)

True Measured

13.0 13.3

0 55

9.0 9.3

True Measured

13.0 13.4

0 8

14.0 15.2

True Measured

13.0 13.7

576 572

0.0 1.6

True Measured

13.0 13.2

576 547

9.0 7.1

True Measured

13.0 13.2

576 557

14.0 13.0

Monitoring point 1 8x 10-G

100 km

Monitoring point 2

100 km

100 km

100 km

ROADM

Tx

DCM 60

DCM 100 DCM 40

DCM 120

DCM 80 20 dB

DCM 110 DCM 120 8x 10-G Rx

100 km

100 km

DCM 60 ROADM

DCM 60

100 km

DCM 60

100 km

Monitoring point 3

FIGURE 7.8 Setup for 10-G NRZ WDM system test. OSNR and CD were monitored at the three tap points. An additional 400 ps/nm dispersion was added at each of the tap points as a further check of CD accuracy. The input power to the monitor was 18 dBm.

7.4 Discussion

187

Table 7.4 Simultaneous OSNR and CD Measurements on 800-km WDM Test Bed Tap Point

CD (ps/nm)

True OSNR (dB)

OSNR Mean Offset (dB)

CD Measured (ps/nm)

1

400 0 400 400 0 400 400 0 400

19.3 19.3 19.3 19.3 19.3 19.3 16.8 16.8 16.8

0.3 0.1 0.4 0.0 0.3 0.4 0.2 0.2 1.0

420 0 þ430 430 0 þ430 172 0 520

2

3

(18.4) (20.5) (20.5) (21.3) (21.3) (21.3) (18.5) (18.6) (18.6)

(0.3) (0.2) (0.1) (0.5) (0.2) (0.1) (0.3) (2.0) (6.0)

(520) (þ400) (520) (þ400)

Note: Values in parentheses show measurements made at 3-dBm power levels.

was varied over three levels using an additional þ/400 ps/nm of dispersion-compensating fiber. A total of 30 measurements of CD and OSNR were made for each level. Measurements were then repeated at different power levels (0 and 3 dBm) to test for the effects of SPM. The true OSNR was measured with an OSA. An independent CD measurement at point 1 was 120 ps/nm and at point 3 was 1000 ps/nm. The latter was outside the training range. The results are summarized in Table 7.4. We see that the mean (systematic) error in the OSNR prediction is less than 0.5 dB for tap points before and after the ROADM and show minimal sensitivity to CD. The degradation in accuracy at monitoring point 3 with 0 and þ400 ps/nm is due to the residual CD falling outside of the training range. The standard deviation in the 30 OSNR measurements for each setting was 0.2 dB. A repeat of the measurements at 3-dBm launch powers again showed excellent agreement with independent OSNR measurements. The measured residual CD at points 1 and 2 was within 10 ps/nm of true CD. The measured residual value at point 3 was in error, because it lay outside the training range. The standard deviation in CD measurements was 25 ps/nm. The CD range increases with 3-dB launch power from 860 to 920 ps/nm and may indicate the onset of SPM effects on CD predictions.

7.4 DISCUSSION In work to date, it has been assumed that the monitor has access to knowledge of the formats and bit rates being monitored from the network management system and can select appropriate predictors from an on-board library. For the monitoring of systems with alien wavelengths, this information may not be available and automatic identification of format would be required. Automatic format and bit rate identification is a feature that the technique is well suited to. As an example, we have demonstrated that a single predictor can predict impairments at both 10.3- and 10.7-Gb/s FEC rates.

188


Table 7.5 Demonstrating Improvement in DGD and CD Accuracy with Increasing Size of Training Set Number of Training Cases

DGDeff Error (ps)

500 750 1000 1250 1500

3.6 2.7 2.5 2.0 1.9

jCDj Error (ps/nm) 17.3 14.1 13.0 11.1 10.4

Table 7.5 shows the improvement in accuracy for 40-Gb/s DPSK obtained with increasing training set size. There is a relatively rapid improvement in performance for the first 1000 training cases, which then plateaus for greater than 2000 cases. In general, however, the required number of training cases will vary depending on the number and range of impairments. Allowing for emulator tuning times, the training time for 2000 cases is approximately 4 h. For the experimental setup described in this paper the measurement time was 1.2 s. This is dominated by the sampling time; the processing time required for prediction takes less than 20 ms. In future implementations, cost-effective solutions at sampling rates of 50 MS/s will be achievable with currently available samples and old technology. This will enable measurement times of better than 50 ms and provide an increased ability to exploit the improvement in accuracy that is achievable through averaging of multiple measurements.

7.4.1 Extension to new impairments In principle, in order to make the technique more robust, one can extend the training set to include other effects that can potentially induce errors, such as optical nonlinearities, filter drift, and delay variations. To illustrate this ability, we have extended the number of simultaneous impairments to include optical filter offset and in-band crosstalk, in addition to OSNR, CD, and DGD (with random g). The results for a simulated 10-Gb/s NRZ signal are shown in Figure 7.9. The filter offset result shows that the technique is able to distinguish between filter-induced dispersion (e.g., from ROADMs) and fiber-induced dispersion. We noted that the mean optical channel power is not used as an input for predictions, and hence the optical filter loss was not being used to measure the offset. The crosstalk introduced into the model was from a single interferer with the same wavelength as the signal. The non-Gaussian statistics of this interference noise enable the machine learning to distinguish the crosstalk from OSNR. Although the crosstalk is determined with only limited accuracy, its presence causes little degradation to the OSNR accuracy.

7.4.2 Application to higher-order formats A key advantage of our approach is that it can be applied without the need for fine tuning of features or modification of hardware. For example, using the same algorithms and hardware as used

7.4 Discussion

OSNR (dB), stdTe = 0.33

absCD (ps/nm), stdTe = 14.66

25

189

DGDeff (ps), stdTe = 1.56 50

1200

40 20

800

30 20

15

400 10

10

0

500 (a)

0

1000

0

500 (b)

In-band crosstalk (db), stdTe = 1.35 30

0

1000

0

500 (c)

1000

Filter offset (GHz), stdTe = 0.64 15

25 10 20 5 15 10

0

500 (d)

1000

0

0

500 (e)

1000

FIGURE 7.9 Simulation results for 10-Gb/s NRZ demonstrating monitoring of simultaneous OSNR, CD, DGD, in-band crosstalk, and optical filter offset. The training ranges were for OSNR, 11–25 dB; CD, 1400 to 1400 ps/nm; and DGD, 0–50 ps (random g), crosstalk 15–24 dB, and filter offset 0–12 GHz. The training set consisted of 2000 random combinations of these impairments. Predictions for 1000 test cases (gray) are shown; true values are shown in black. The RMS error at the 2s level are (a) OSNR 0.3 dB, (b) absCD 15 ps/nm, (c) DGD 1.6 ps, (d) crosstalk 1.4 dB, and (e) filter offset 0.7 GHz.

for 10-Gb/s NRZ and 40-Gb/s DPSK, we find that simulations of 40-Gb/s RZ-DQPSK (see Figures 7.10 and 7.11(a)–(c)) give results that are consistent with the NRZ and DPSK results for simultaneous CD and DGDeff presented in this chapter. In addition, we note that ability of machine learning techniques to discern patterns in very “unstructured” phase portraits holds promise for applying the technique to monitoring polarization-multiplexed systems. By way of example, in Figure 7.11(d, e) we show the effects of CD and DGD on the phase portrait for a simulated 80-Gb/s polarization-multiplexed RZ-QPSK. We emphasize that these portraits have been obtained assuming the same direct detection–based monitor (25-ps delay and 20-GHz receiver) used for 10-Gb/s NRZ signals.

190


absCD (ps/nm), stdTe = 15.1

DGDeff (ps), stdTe = 1.45 25

800 40-Gb/s DQPSK

20 600 15 400

10

200 0

5 0

100

200

300

0

0

(a)

100

200

300

(b)

FIGURE 7.10 Simulation results for simultaneous measurements of absCD and DGDeff for 40-Gb/s RZ-DQPSK. The training set contained 2000 cases with OSNR ranging from 14 to 28 dB; CD, 800 to 800 ps/nm; and DGD, 0 to 25 ps.


(a)

(b)



(d)

(e)


40-Gb/s DQPSK


(c)

80-Gb/s PM-DQPSK


(f)

FIGURE 7.11 Simulated phase portraits for (a–c) 40-Gb/s RZ-DQPSK and (d–f) 80-Gb/s polarization-multiplexed DPSK showing the effects of CD and DGD. The OSNR is 14 dB for all cases. The tap delay was set at 25 ps for both bit rates. The impairment levels were DGDeff (ps), absCD (ps/nm): (a) 0,0, (b) 25,0, (c) 0,800, (d) 0,0, (e) 25,0, and (f) 0,800.

References

191

7.5 SUMMARY The asynchronous delay-tap sampling technique is a promising in-service monitoring technique capable of measuring simultaneous impairments for multiple bit rates and formats. The two core components of the technique are the characterization of the signal with an asynchronously sampled phase portrait and the extraction of impairment features using statistical machine learning techniques. A major advantage of the technique is that a direct detection–based receiver with a set bandwidths can be used for multiple formats and bit rates. In this chapter we have reviewed the technique beginning with a discussion and interpretation of the phase portrait and a brief overview of some pattern recognition approaches demonstrating its application to NRZ, DPSK, and QPSK formats for simultaneous measurements of OSNR, CD, and DGDeff. Early proofs of principle demonstrations, based on single transponder- and laboratorygenerated impairments, have been further validated at 10-Gb/s with WDM system measurements. Key challenges for practical realization of the technique include a demonstration of robustness with respect to transponder variations and compatibility with ROADMs. We find that robustness to transponder variations can be improved by training over multiple transponders. Similarly, the ability to distinguish dispersion in the presence of ROADM filter effects may be enhanced by including filter variations in the training set. Finally, our experience to date suggests that the remarkable ability of machine learning techniques to discern patterns in extremely “unstructured” phase portraits holds promise for application of the technique to the monitoring of advanced 100-Gb/s formats and the “next generation” of impairments.

REFERENCES 1. Kilper DC, Bach R, Blumenthal DJ, Einstein D, Landolsi T, Ostar L, et al. Optical performance monitoring. J Lightwave Technol 2004;22:294–304. 2. Kaminow IP, Li T, Willner AE. Optical performance monitoring. In: Optical fiber telecommunications V B. New York, New York: Academic Press; 2008. 3. Dods SD, Anderson TB. Optical performance monitoring technique using delay tap asynchronous waveform sampling. In: Proc. OFC/NFOEC, paper OThP5. Anaheim, California; 2006. 4. Anderson T, Dods SD, Clarke K, Bedo J, Kowalczyk A. Multi-impairment monitoring in photonic networks. In: Proc. ECOC, paper 3.5.1. Berlin, Germany; 2007. 5. Ji H, Park K, Lee J, Chung H, Son E, Han K, et al. Optical performance monitoring techniques based on pilot tones for WDM network applications. J Opt Network 2004;3:510–33. 6. Skoog RA, Banwell TC, Gannett JW, Habiby SF, Pang M, Rauch ME, et al. Automatic identification of impairments using support vector machine pattern classification on eye diagrams. IEEE Photon Technol Lett 2006;18:2398–400. 7. Hanik N, Gladisch A, Caspar C, Strebel B. Application of amplitude histograms to monitor performance of optical channels. Electron Lett 1999;35:403–4. 8. Kozicki B, Takuya O, Hidehiko T. Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis. J Lightwave Technol 2008;26:1353–61. 9. Geyer JC, Hauske FN, Fludger CRS, Duthel T, Schulien C, Kuschnerov M, et al. Channel parameter estimation for polarization diverse coherent receivers. IEEE Photon Technol Lett 2008;20(10):776–8.

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10. Mayrock M, Haunstein H. Performance monitoring in optical OFDM systems. In: Proc. OFC/NFOEC, paper OWM3. San Diego, California; 2009. 11. Anderson T, Clarke K, Beaman D, Ferra H, Birk M, Guodong Z, et al. Experimental demonstration of multi-impairment monitoring on a commercial 10 Gb/s NRZ channel. In: Proc. OFC/NFOEC, paper OThH7. San Diego, California; 2009. 12. Kozicki B, Maruta A, Kitayama K. Experimental demonstration of optical performance monitoring for RZ-DPSK signals using delay-tap sampling method. Opt Expr 2008;16:3566–76. 13. Kozicki B, Maruta A, Kitayama K. Asynchronous optical performance monitoring of RZ-DQPSK signals using delay-tap sampling. In: Proc. ECOC, paper P060. Berlin, Germany; 2007. 14. Choi HY, Takushima Y, Chung YC. Multiple impairment monitoring technique using optical field detection and asynchronous delay-tap sampling method. In: Proc. OFC/NFOEC, paper 0ThJ5. San Diego, California; 2009. 15. Ku YC, Chan CK. High-speed data and pulse-carver alignment in RZ-OOK systems using delay tap asynchronous waveform sampling. In: Proc. ECOC, paper Tu4.2.2. Cannes, France; 2006. 16. Kantz H, Schreiber T. Nonlinear time series analysis. Cambridge and New York: Cambridge University Press; 2004. 17. Zhao J, Lu C, Lam K-M, Li Z, Tam HY, Wai PKA. A novel optical signal monitoring method of DPSK signal based on delay tap sampling and Hausdorff distance measure. In: Proc. CLEO/QELS, paper JWA108. San Jose, California; 2008. 18. Jargon JA, Wu X, Willner AE. Optical performance monitoring using artificial neural networks with features derived from asynchronous delay tap sampling. In: Proc. CLEO/QELS, paper OThH1. San Jose, California; 2009. 19. Vapnik V. Statistical learning theory. New York, New York: Wiley; 1998. 20. Duda RO, Hart PE, Stork DG. Pattern classification. New York, New York: Wiley; 2000. 21. Kogelink H, Jopson RM, Nelson LE. Polarization-mode dispersion. In: Kaminow I, Li T, editors. Optical fiber telecommunications, IVB. New York: Academic Press; 2002. 22. Cornick KE, Boroditsky M, Finch S, Dods SD, Farrell PM. Experimental comparison of PMD-induced system penalty models. IEEE Photon Technol Lett 2006;18:1149–51. 23. Anderson T, Beaman D, Li JC, Jerphagnon O, Le Rouzic E, Neddam F, et al. Demonstration of simultaneous OSNR and CD monitoring using asynchronous delay tap sampling on an 800 km WDM test bed. In: Proc., paper 9.3.4 ECOC. Vienna, Austria; 2009. 24. Clarke K, Anderson T, Dods SD. Monitoring of multiple modulation formats using asynchronous delay-tap sampling. In: Proc. ACOFT, paper MoA1–2. Melbourne, Australia; 2007.

CHAPTER

Optical performance monitoring based on linear optical sampling

8 Christophe Dorrer

Laboratory for Laser Energetics, University of Rochester, Rochester, New York

8.1 INTRODUCTION

8.1.1 Data encoding in the electric field of optical waves Modulating and measuring the phase of optical waves are enablers in a wide variety of applications in optics. In optical telecommunications, optical waves with time-varying instantaneous power I(t) and phase c(t) are used to transmit information between a transmitter and receiver. Although these quantities are usually thought p offfiffiffiffiffiffiffi as independent, it is better to think in terms of the electric field of the modulated waves EðtÞ ¼ IðtÞ exp½icðtÞ since this is really the physical quantity of interest. Encoding information in the electric field of optical waves is attractive for several reasons.1,2 Phase-modulated signals operate with constant or periodic intensity, and the deleterious effects of nonlinear propagation can be reduced relatively to the effects experienced by signals based on amplitude modulation. Various discrete phase levels can be encoded and decoded unambiguously. Phasemodulated formats can be spectrally efficient, in the sense that good transmission properties are obtained while using less bandwidth than an on-off keying (OOK) system. This is particularly important for WDM applications. Phase modulation can be combined with amplitude modulation to convey multiple information bits per time slot. Figure 8.1 displays examples of data-encoded optical waveforms: on the left side, the temporal intensity and phase are plotted independently, while on the right side, a constellation diagram represents the distribution of the electric field in the complex plane at the center of the time slot. The OOK signal in Figure 8.1(a) uses the absence or presence of energy in the time slot to encode binary information, and does not rely on phase modulation to carry information. The binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) signals in Figures 8.1(b) and (c) only use phase encoding to encode 1 bit or 2 bits in a given time slot, respectively. The quadrature amplitude modulation (QAM) signal in Figure 8.1(d) relies on amplitude and phase modulation of the electric field to encode information. Since square-law photodetectors are only sensitive to the intensity of the electric field, one cannot directly measure the phase of an optical wave and directly decode a phase-modulated signal by photodetection. However, interference of two optical waves leads to an intensity that depends on the relative phase of the two waves. For example, spectral interferometry uses the spectrally resolved interference between two optical waves to recover their spectral phase difference as a function of the optical frequency, while spatial interferometry can measure the phase difference between two optical © 2010 Elsevier Inc. All rights reserved. Doi: 10.1016/B978-0-12-374950-5.00008-0

193

194

CHAPTER 8 Optical performance monitoring based on linear optical sampling

(a) t (b)

π t π/2

(c) t (d) t

FIGURE 8.1 Data-encoded optical signals represented by intensity and phase as a function of time (left column) and complex electric field at center of time slot (right column). The signals correspond to (a) on-off keying, (b) binary phase-shift keying, (c) quaternary phase-shift keying, and (d) 16-state quadrature amplitude modulation.

waves as a function of the spatial variables. The information encoded in an optical telecommunication signal can be recovered by measuring the interferometric beating between the signal and the local oscillator (LO)—that is, a reference wave provided by the receiver. One can also generate a delayed replica of the signal, and interfere the signal and its delayed replica to recover the phase difference between successive bits of the data without a local oscillator. This is generally known as differential phase-shift keying, and popular corresponding telecommunication formats are DPSK (two phase levels) and DQPSK (four phase levels).

8.1.2 Temporal characterization of optical signals The characterization of time-varying optical signals has been a challenging problem since the invention of lasers. This is due to the lack of phase sensitivity of square-law photodetectors and the relatively slow response times of electronic phenomena when compared to optical phenomena. The second-order intensity autocorrelation is a well-known approach to obtain temporal information about optical signals. Its measurement is simple: two time-delayed replicas of the test signal are generated and sent to a nonlinear medium properly phase matched for up-conversion. The energy of the up-converted signal is measured as a function of the interreplica relative delay. For isolated optical pulses, a functional form is often assumed for the temporal intensity (e.g., Gaussian or secanthyperbolic), in which case the intensity duration can be estimated from the duration of the intensity autocorrelation. The intensity autocorrelation only gives indirect information about the instantaneous power of the test signal and does not contain any phase information. It can, however, be used in

8.1 Introduction

195

advanced optical performance monitoring, as intensity correlation between different times in a data stream can be used to quantify impairments.3 Indirect approaches have been devised for the characterization of the electric field of optical signals.4,5 The optical signal is modified in various controlled ways; for example, it is gated by a modulator or by a nonlinear interaction with a short optical pulse, filtered in the spectral domain, or sent in a dispersive element. The energy of the modified signal is then measured with a timeintegrating photodetector. The experimental trace is composed of the measured energies as a function of the parameters of the different filters that have been used (e.g., the delay between the pulse and the temporal modulation, or the center frequency of the passband of a spectral filter). Under some conditions, the electric field of the signal (intensity and phase) can be retrieved from the measured experimental trace. Many techniques based on different principles have been demonstrated in the field of ultrafast optics. For example, frequency-resolved optical gating (FROG) uses nonlinear optics to measure a spectrogram of the test source, from which the electric field can be reconstructed using iterative blind-deconvolution phase-retrieval algorithms.6 The spectrogram is measured by nonlinear interaction of several replicas of the test source, such as sum-harmonic generation or four-wave mixing; the optical spectrum of the nonlinear signal is measured as a function of the delay between the replicas and the optical frequency. Another popular technique, spectral phase interferometry for direct electric-field reconstruction (SPIDER), uses the interference of two spectrally sheared test pulses to directly reconstruct the spectral phase of the test source.7 The spectral shear is obtained by nonlinear conversion with a chirped optical pulse, and the spectral phase is reconstructed by Fourier processing of a spectral interferogram measured for example by a spectrometer. These techniques rely on nonlinear optics, and their application to signals in the telecommunication environment can be difficult. Linear techniques based on similar principles but implemented with telecommunication components have also been demonstrated for use with lowaverage power sources.8,9 While these techniques have been successfully applied to repetitive optical signals, including pulses in the telecommunication environment, they would be difficult to use for single-shot measurements of nonrepetitive events such as data-encoded telecommunication signals. There are few reports of the measurement of such signals. One approach for single-shot characterization of the intensity of an optical signal is to use time magnification, a general process by which a temporally scaled copy of the test waveform is generated.10,11 This scaling is generally obtained using a concept similar to spatial imaging with magnification—that is, by using quadratic temporal and spectral phase modulations to mimic the effect of lenses and free-space propagation. A quadratic temporal phase modulation can be induced by a temporal phase modulator driven by a sinusoidal drive provided that the optical pulse is synchronized with one of the extrema of the modulation. It can also be obtained by nonlinear interaction with a chirped optical pulse. The quadratic spectral phase modulation can be obtained simply by propagation in a dispersive fiber. Temporal magnification was extended to the single-shot characterization of the electric field using heterodyning with a monochromatic source.12 Characterizing fast optical phenomena remains difficult for nonrepetitive optical signals. For these signals, it is mandatory to measure a meaningful experimental trace in a single shot. The measurement of temporal samples of the time-varying instantaneous power of a data-encoded optical signal is often used in the telecommunication environment. It is typically performed with a gate having a bandwidth higher than the bandwidth of the signal in order to temporally resolve the intensity of the optical wave. The measured samples are then plotted in a statistical representation

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(e.g., an eye diagram), and performance can be assessed using various metrics. Photodetection and analog-to-digital conversion typically have a bandwidth lower than 100 GHz. High-bandwidth optical sampling systems have been implemented where the test optical signal is gated by a high-bandwidth, low-repetition-rate gate, and the energy of the resulting pulse is measured using a slower analog-to-digital converter.4,13 The corresponding sample is representative of the instantaneous power of the test signal at the time defined by the gate. Higher bandwidths can be obtained using nonlinear interactions with short optical pulses, including sum-frequency generation and four-wave mixing. By itself, this approach can at best provide temporal samples of the intensity that can be grouped into an eye diagram. No phase information is generally obtained by sampling techniques, whether they are based on photodetection, temporal modulators, or nonlinear interactions.

8.1.3 Linear optical sampling Wave interference is a well-known physical phenomenon to perform phase-to-intensity conversion. By the interference of two optical waves, the relative phase difference between the waves becomes apparent in the resulting intensity—that is, the phase of a test optical source can be obtained by beating with a reference optical source, a process known as coherent detection, homodyning, or heterodyning. Coherent photodetection is the basis of linear optical sampling (LOS): characterization of the temporal electric field of an optical source is obtained by interference with a local oscillator and photodetection. The local oscillator can be a quasimonochromatic source, in which case the interference must be measured with fast photodetectors. The local oscillator can also be a short optical pulse providing the necessary temporal resolution, in which case the interference can be resolved with slower photodetectors. Diagnostics based on this principle are advantageous since they can in principle provide a high-resolution temporal representation of the electric field of a test source. This information is valuable for optical performance monitoring since the properties of the signal can be assessed in a somewhat general fashion. Coherent detection is used extensively for optical telecommunications.2 This aspect is not described in this chapter, which focuses on the application of coherent detection, particularly with a short optical pulse, as a diagnostic for optical performance monitoring. The principle of LOS is presented in Section 8.2. Section 8.3 describes experimental implementations of LOS. Section 8.4 presents various experimental results demonstrating the application of LOS to optical performance monitoring of telecommunication signals and characterization of devices and fibers. Finally, Section 8.5 is a nonexhaustive description of related techniques and recent results.

8.2 LOS PRINCIPLE AND PROPERTIES 8.2.1 Coherent detection

The real electric field e of an optical wave is a solution of Maxwell’s equations. It is convenient to work with the analytic signal E, which verifies eðtÞ ¼ EðtÞ þ E ðtÞ:

(8.1)

8.2 LOS principle and properties

197

The analytic signal is complex, and therefore can be expressed uniquely in terms of an amplitude and phase, EðtÞ ¼jEðtÞjexp½icðtÞ þ ic0 io0 t;

(8.2)

where jE(t)j is the time-dependent field amplitude (i.e., the envelope of the field oscillations), o0 is the carrier frequency (usually chosen near the center of the pulse spectrum), c(t) is the time-dependent phase, and c0 is a constant, known as the carrier-envelope offset (CEO) phase. The square of the envelope, I(t) ¼jE(t)j2, is the time-dependent instantaneous power of the pulse that can be measured if a detector of sufficient bandwidth is available (note that absolute measurement of the instantaneous power is usually not required). The temporal phase takes into account the changes in the frequency of the field as a function of time via the instantaneous frequency O ¼ @c/@t. The CEO phase describes the relative location of the field envelope and carrier, which is usually different from pulse to pulse, even in a pulse train from a mode-locked laser unless active stabilization is used.14 The frequency representation of the analytic signal is defined by the Fourier transform, ð þ1 ~ ~ dtEðtÞ expðiotÞ: (8.3) EðoÞ ¼j EðoÞjexp½i’ðoÞ ¼ 1

~ Here j EðoÞ j is the spectral amplitude and ’(o) is the spectral phase. The square of the spectral 2 ~ ~ , is the spectral density, which can be obtained with an optical spectrum amplitude, IðoÞ ¼j EðoÞj analyzer. The spectral phase describes the relative phase of the optical frequencies composing the pulse, and its derivative @’/@o is the group delay T(o) at the corresponding frequency—that is, the time of arrival of a subset of optical frequencies of the pulse around o. A pulse with a constant group delay—that is, a linear spectral phase—is said to be Fourier-transform limited because it is the shortest pulse that can be obtained for a given optical spectrum.15 Expressions for the LOS signal with various local oscillators are obtained using the notations of Figure 8.2. The interference of two optical waves with fields E1(t) and E2(t) leads to the intensity I12 ðtÞ ¼ j E1 ðtÞ þ E2 ðtÞj2 ¼ I1 ðtÞ þ I2 ðtÞ þ 2p Re½E 1 ðtÞE2ffi ðtÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ I1 ðtÞ þ I2 ðtÞ þ 2 I1 ðtÞI2 ðtÞ cos½c1 ðtÞ c2 ðtÞ:

(8.4)

The phase difference c1 – c2 between the two waves is therefore revealed as an intensity modulation after interference. If E1 is a phase-encoded telecommunication signal, E2 can be a local oscillator generated at the receiver, or E2 can be a delayed version of E1. For LOS, the local oscillator is an independent optical wave provided by the diagnostic, and the fields are written E2 ¼ ELO and E1 ¼ EDATA. When using a 90-degree optical hybrid, as pictured in Figure 8.2, the two fields are split at the splitters SLO and SDATA and recombined at the combiners CA and CB. The interference measured with a square-law photodetector with impulse response R on one output of the combiner CA is Ð þ1 SA;1 ðtÞ ¼ 1 Rðt tÞ j EDATA ðtÞ þ ELO ðtÞj2 dt Ð þ1 Ð þ1 Ð þ1 ¼ 1 Rðt tÞIDATA ðtÞdt þ 1 Rðt tÞILO ðtÞdt þ 2 1 Rðt tÞRe½EDATA ðtÞELO ðtÞdt; (8.5)

198


Data source

CA

SLO

Local oscillator

– SA

SDATA

CB

– SB

p/2 phase shift

FIGURE 8.2 Layout for the direct measurement of the complex interference between data source and local oscillator. The two sources are split at the splitters SLO and SDATA, and recombined pairwise at the combiners CA and CB. Balanced photodetection of the two outputs of each combiner yields the in-phase and quadrature components of the interference if a relative p/2 phase shift is introduced in the optical path of one of the two sources between splitters and combiners.19 Copyright © 2006 OSA.

where Re(z) represents the real part of the complex argument z. The signal measured on the other port of the combiner CA is Ð þ1 SA;2 ðtÞ ¼ 1 Rðt tÞ j EDATA ðtÞ ELO ðtÞj2 dt Ð þ1 Ð þ1 Ð þ1 ¼ 1 Rðt tÞIDATA ðtÞdt þ 1 Rðt tÞILO ðtÞdt 2 1 Rðt tÞ Re½EDATA ðtÞELO ðtÞdt: (8.6) Balanced photodetection isolates the interferometric term from the phase-independent terms, leading to the signal SA ðtÞ ¼ SA;1 ðtÞ SA;2 ðtÞ Ð þ1 ¼ 4 1 Rðt tÞ Re½EDATA ðtÞELO ðtÞdt:

(8.7)

Since the recovery of the complex interference from its real part is in general ambiguous, one usually arranges for the simultaneous measurement of its imaginary part. This can be achieved by adding a relative p/2 phase shift on one of the fields before interference. Balanced photodetection at the two outputs of combiner CB leads to SB ðtÞ ¼ SB;1 ðtÞ SB;2 ðtÞ Ð þ1 ¼ 4 1 Rðt tÞ RefEDATA ðtÞ½iELO ðtÞ gdt Ð þ1 ¼ 4 1 Rðt tÞ Im½EDATA ðtÞELO ðtÞdt:

(8.8)


199

It is customary to call the quantities SA and SB the in-phase and quadrature component of the interference between the two sources, which are also known as the I and Q components. SA and SB can be combined to obtain the complex interference of the data and local oscillator: SðtÞ ¼

ð þ1

1 Rðt tÞEDATA ðtÞELO ðtÞdt ¼ ½SA ðtÞ þ iSB ðtÞ: 4 1

(8.9)

Balanced photodetection benefits from the intrinsic cancellation of the intensity of each source. For practical reasons, particularly if high-speed photodetectors are required, a single photodetector might be used instead of a balanced photodetector. In this case, it is not possible to extract one of the quadratures from a single sample described by Equation (8.5). However, it is possible to extract the interference between the two sources if a large collection of samples is obtained, and the average value of these samples is removed. Since the average value of the interference term is zero for uncorrelated sources, one obtains SðtÞ ¼

ð þ1 1

1 Rðt tÞEDATA ðtÞELO ðtÞdt ¼ f½SA;1 ðtÞ hSA;1 ðtÞi þ i½SB;1 ðtÞ hSB;1 ðtÞig; 2

(8.10)

where the average value of the two measured signals (denoted by brackets) is calculated on a large collection of measured samples. In practice, the removal of the constant terms of each collection of samples can be performed by AC-coupled photodetectors, since they naturally remove any timeindependent offset on the measured signals.

8.2.2 Various implementations of coherent detection for optical performance monitoring For optical performance monitoring, various implementations of coherent detection are now described in a schematic way to emphasize their general properties (Figure 8.3).

Signal

CW LO

Pulsed LO

Signal

Frequency (a)

Signal

Pump pulse

Frequency (b)

Idler

CW LO

Frequency (c)

FIGURE 8.3 Spectral representation of the sources involved in various implementations of linear optical sampling. (a) the Monochromatic local oscillator is spectrally overlapped with the signal. (b) the Broadband pulsed local oscillator is overlapped with the signal. (c) the Nonlinear interaction between the short pump pulse and the signal leads to an idler pulse, and the monochromatic signal is overlapped with the generated idler.

200


8.2.2.1 Coherent detection with a monochromatic local oscillator The local oscillator can be a monochromatic source at the optical frequency oDATA (Figure 8.3(a)), with ELO(t) ¼ exp(ioDATAt) (cases where the local oscillator frequency is not exactly oDATA or is time varying are treated later).16,17 With photodetectors having an impulse response faster than the temporal features of the data electric-field envelope, the measured signal is ð þ1 SðtÞ ¼jEDATA ðtÞjexp½icDATA ðtÞ RðtÞdt: (8.11) 1

The signal measured at time t is therefore a representation of the data electric field at that time. A continuous representation of the electric field can be obtained by continuously measuring the signals SA and SB and combining them. This approach is identical to regular homodyne coherent detection in telecommunication systems, except that in practice, no phase-locking is required provided that the phase can be tracked numerically. The bandwidth of a diagnostic based on coherent detection with a monochromatic local oscillator is fundamentally limited by the bandwidth of the photodetectors since the role of the monochromatic LO is only to provide phase sensitivity.

8.2.2.2 Coherent detection with a short optical pulse If the local oscillator is a short optical pulse (Figure 8.3(b)), it can be used to measure the electric field of the signal with high temporal resolution.18,19 If the impulse response of the photodetectors is much longer than the duration of the local oscillator pulse, ð þ1 EDATA ðtÞELO ðtÞdt: (8.12) SðtÞ ¼ RðtÞ 1

The signal therefore has the shape of the photodetection impulse response and a complex amplitude proportional to the overlap of the electric fields of the data source and LO pulse. The two quadrature signals can be sampled with low-speed, analog-to-digital converters to obtain a highresolution sample of the electric field. The diagnostic’s bandwidth is set by the duration of the local oscillator, and not by the photodetectors. Sources with rates as high as 640 Gb/s have been characterized with a pulsed local oscillator. The data electric field is gated by the interference with the LO, and LOS is therefore fundamentally different from other sampling techniques for which gating of the intensity is performed. Samples are only obtained when a local oscillator pulse is present in the interferometer. This situation is common when sampling rapidly varying signals: high-bandwidth gates are usually obtained at a somewhat low repetition rate. Multiple samples are obtained when a train of sampling pulses is used: each pulse samples the data at a different time and a statistical representation is obtained by combining the measured samples. Photodetectors with response slower than the duration of the sampling pulses but faster than the period of the sampling train must be used to ensure independent measurements of successive samples of the data field. To understand some fundamental properties of LOS with a short local oscillator, it is particularly Ð þ1 instructive to consider the integral 1 EDATA ðtÞELO ðtÞdt in the frequency domain: ð þ1 ð þ1 do : (8.13) EDATA ðtÞELO ðtÞdt ¼ E~DATA ðoÞE~LO ðoÞ 2p 1 1


201

Equation (8.13) shows that a LOS signal is obtained only if the local oscillator spectrum overlaps with the data spectrum. This property arises from the low-bandwidth homodyne photodetection: Since only slow signals can be measured by time-integrating photodetectors, only the interference of one optical frequency of the test source with the same optical frequency of the local oscillator participates in the signal. Consequently, when the local oscillator has a constant spectral density over the spectral support of the data source, Equation (8.13) simplifies to ð þ1 ð þ1 do ¼jEDATA ð0Þj exp½icDATA ð0Þ; EDATA ðtÞELO ðtÞdt / E~DATA ðoÞ 2p 1 1 which means that the sample measured by a short local oscillator arriving at t ¼ 0 is directly proportional to the data temporal electric field at t ¼ 0. The influence of the LO field can be calculated directly from Equation (8.13); this is done in Reference 20 for a Gaussian LO with a second-order dispersion.

8.2.2.3 Phase-preserving temporal gating and monochromatic local oscillator Sampled coherent detection has also been implemented with distinct gating and homodyning sources (Figure 8.3(c)). In this case, the gating source must provide the temporal resolution while preserving phase information. An appropriate process for this operation is four-wave mixing, for example, implemented in a highly nonlinear fiber.21 The data signal EDATA(t) is mixed with a high-energy pump pulse EPUMP(t) and generates an idler signal described by the electric field EIDLER ðtÞ ¼ E2PUMP ðtÞEDATA ðtÞ ¼ IPUMP ðtÞj EDATA ðtÞj exp½2icPUMP ðtÞ icDATA ðtÞ 2ioPUMP t þ ioDATA t:

(8.14)

The idler is therefore generated around the optical frequency oIDLER ¼ 2oPUMP – oDATA, and its phase verifies cIDLER(t) ¼ 2cPUMP(t) – cDATA(t), therefore ensuring that the data phase is related to the measured idler phase. When the pump pulse is significantly shorter than the data signal, the idler field simplifies to EIDLER ðtÞ ¼ IPUMP ðtÞj EDATA ð0Þj exp½2icPUMP ðtÞ icDATA ð0Þ ioIDLER t:

(8.15)

Coherent detection can be performed using a local oscillator around the frequency oIDLER and a photodetector with bandwidth higher than the repetition rate of the sampling source but lower than the bandwidth of the idler signal to obtain the signal: ð þ1 IPUMP ðtÞ exp½2icPUMP ðtÞdt: (8.16) SðtÞ ¼ RðtÞjEDATA ð0Þj exp½icDATA ð0Þ 1

The signal of Equation (8.16) can be sampled with a slow analog-to-digital converter to obtain a sample of the complex conjugate of the data electric field. In this approach, temporal resolution is provided by the pump pulse, while phase sensitivity is provided by the monochromatic LO. This can be used to decouple the requirements imposed on these two sources.

8.2.3 Polarization and wavelength sensitivity Because sampled coherent detection is based on the linear interference of the signal with the local oscillator, the two sources must be copolarized when reaching the photodetectors for optimal operation, although they might have different polarization states at the input or inside the diagnostic, such

202


as when different polarization states are used in a polarization interferometer. This point is of particular practical importance since polarization mode dispersion in the non-polarization-maintaining optical fibers used in deployed optical networks leads to time-varying polarization states. Laboratory experiments usually rely on polarization controllers to adjust the polarization state of the test source, but for complete characterization of a source in the general case, polarization diversity—that is, a setup characterizing the source along two orthogonal states—must be implemented. In LOS, two identical setups operating on two polarization states of the data field can be used. Polarization sensitivity is common in other high-speed optical sampling techniques, since most nonlinear effects are polarization dependent. Coherent detection is also sensitive to the spectral overlap of the test source and local oscillator. With a monochromatic local oscillator, if the local oscillator frequency departs significantly from the center of the spectrum of the data source, high-frequency oscillations occur in the signal to be measured, which increases the bandwidth requirement for the photodetectors. With the low-speed photodetectors used in sampled coherent photodetection with a pulsed local oscillator, the local oscillator must have a spectrum encompassing the spectral support of the test source. In the absence of overlap, no signal is observed. This can be used to selectively characterize wavelength-multiplexed channels: Following Equation (8.13), the spectral support of the LO can be chosen to overlap with the spectral support of a given channel to characterize this channel. With the absence of spectral overlap with other channels present at the input of the diagnostic, these channels are not sampled.

8.2.4 Phase sensitivity 8.2.4.1 Monochromatic local oscillator Unless the same optical source is used to generate the data signal and the local oscillator (which constrains the transmitter and diagnostic to be collocated) or active phase-locking is used, the monochromatic local oscillator beating with the test source does not exactly cancel the carrier frequency of the test source. For a monochromatic frequency oLO and frequency noise doLO(t), the electric field of the local oscillator is described by ðt doLO ðt0 Þdt0 : (8.17) ELO ðtÞ ¼ exp½ioLO t i 1

Ðt Identically, the data have their own phase noise represented by the integral 1 doDATA ðt0 Þdt0 . Equation (8.11) can be written as ðt ½doLO ðt0 Þ doDATA ðt0 Þdt0 g; (8.18) SðtÞ ¼j EDATA ðtÞ j expficDATA ðtÞ þ iðoLO oDATA Þt þ i 1

where a proportionality constant has been omitted. Equations (8.18) and (8.11) differ by a linear temporal phase due toÐ the difference in the average optical carrier of the two sources (oLO – oDATA)t t and by a noise term 1 ½doLO ðt0 Þ doDATA ðt0 Þdt0 . The carrier-difference term is a systematic rotation at a constant rate in the complex plane and the noise term is a slowly-varying function, so that the induced phase terms can in practice be tracked and removed, therefore allowing the recovery of the data phase.


203

8.2.4.2 Pulsed local oscillator For a pulsed local oscillator, one considers a train of sampling pulses described by the electric field 1 X n¼1

ðt j ELO ðt nTÞj exp½icLO ðt nTÞ ioLO t i

doLO ðt0 Þdt0 :

(8.19)

1

This train of pulses is represented by a periodic modulation with period T of a high-frequency carrier with mean frequency oLO. Because oLOT is not necessarily a multiple of 2p, the electric field of Equation (8.19) is not periodic even in the absence of noise. Only the envelope of the field is periodic, as pictured in Figure 8.4. Precise phase stabilization is possible,14 but digital processing allows implementing coherent detection without active stabilization. In the absence of phase noise (i.e., doLO being identically zero), in the referential of each pulse centeredÐ at t ¼ nT, the t phase difference between consecutive pulses is a constant. With the phase noise 1 doLO ðt0 Þdt0 , there is a slow drift of the phase when considering the time interval corresponding to a large number of sampling pulses. For all practically useful sources, there is no significant random change of the instantaneous frequency over the temporal support of each sampling pulse and across a sequence of successive pulses. When the photodetectors have bandwidth higher than the repetition rate of the train of pulses but much slower than the duration of the sampling pulses, the output is a collection of discrete samples corresponding to the sampling of the electric field of the test source at times nT. Using Equations (8.12) and (8.19), the nth sample is proportional to ð nT ½doLO ðt0 Þ doDATA ðt0 Þdt0 g: Sn ¼j EDATA ðnTÞ j expficDATA ðnTÞ þ inTðoLO oDATA Þ þ i 1

(8.20) The discrete samples measured with a pulsed local oscillator at a low repetition rate T are therefore expressed similarly to the samples measured with a monochromatic local oscillator and continuous acquisition. With a pulsed local oscillator, LOS samples are therefore samples of the electric field of the data source measured at a rate given by the repetition rate of the sampling source. It is obvious that j Sn j2 ¼ IDATA ðnTÞ;

(8.21)

which means that the intensity of the data source is perfectly sampled if the local oscillator spectrum encompasses the spectrum of the data signal.22,23 However, T

T

Time

FIGURE 8.4 Train of sampling pulses in the time domain showing the carrier-phase evolution under the field envelope.

204


argðSn Þ ¼ cDATA ðnTÞ þ ðoLO oDATA ÞnT þ

ð nT 1

½doLO ðt0 Þ doDATA ðt0 Þdt0 :

(8.22)

The phase (oLO – oDATA)nT induces a rotation of the samples in the complex plane occurring at a constant rate. One should note that unless special care is taken in setting oLO ¼ oDATA, these quantities are different, and rotation is therefore boundÐ to occur. The finite linewidth of the two sources nT impacts the recovered phase through the integral 1 ½doLO ðt0 Þ doDATA ðt0 Þdt0 . This integral modifies the rotation rate since the induced rotation rate (angle per sample) is approximately equal to T [doLO(nT) – doDATA(nT)]. Phase tracking can be used to remove these effects. However, it should be noted that since samples are measured at a much lower rate than with continuous sampling, phase tracking is more difficult for identical linewidth of the considered sources. With continuous sampling, a sampling rate of the order of 10 GSamples/s is possible, while mode-locked sources appropriate for pulsed coherent detection usually have repetition rates around 100 MHz.

8.2.4.3 Pulsed gate and monochromatic local oscillator For a pulsed sampling source described by Equation (8.19) and a local oscillator described by Equation (8.17), the measured signal is Ð nT Sn ¼ jEDATA ðnTÞj expficDATA ðnTÞ inTð2oPUMP oDATA oLO Þ i 1 : (8.23) ½2doPUMP ðt0 Þ doDATA ðt0 Þ doLO ðt0 Þdt0 g: Identical considerations relate to the phase terms of Equation (8.23): There is a constant-rate rotation Ð nT induced by the term nT(2oPUMP – oDATA – oLO) and phase drifts due to 1 ½2doPUMP ðt0 Þ doDATA ðt0 Þ doLO ðt0 Þdt0 . Each optical source contributes to the noise, and there are therefore three sources of phase noise in this case, but phase tracking techniques identical to other implementations of coherent detection can be used.

8.2.5 Digital phase tracking The collection of complex samples obtained with coherent sampled detection can be expressed as Sðtn Þ ¼jEDATA ðtn Þj exp½icDATA ðtn Þ þ iOtn þ iyðtn Þ;

(8.24)

where O is a rotation rate assumed constant over the collection of samples, y is a slowly-varying function, and tn is a temporal variable set by the sampling rate of the acquisition system. Since one is interested in the electric field of the data signal, methods for the removal of the phase terms Otn þ y(tn) must be used. Figure 8.5 illustrates the effect of the various terms of Equation (8.24) with a set of experimental data measured on a BPSK signal. In Figure 8.5(a), the measured phase before processing is shown. In Figure 8.5(b), the linear term has been removed, leading to a slowly-varying phase evolution on which the binary data modulation is superimposed. In Figure 8.5(c), the slowlyvarying phase has been removed, and the two phase levels separated by p can be seen. Digital phase tracking can be performed in various ways. One can observe, for example, that arg½Sðtnþ1 Þ arg½Sðtn Þ ¼ ½cDATA ðtnþ1 Þ cDATA ðtn Þ þ Oðtnþ1 tn Þ þ ½yðtnþ1 Þ yðtn Þ:

(8.25)

For phase-encoded signals with discrete phase levels (e.g., BPSK, QPSK, . . .), the first term in the rightside of Equation (8.25) corresponds to discrete differential phase levels. The second term is a

8.3 Implementations of LOS

205

Phase (rad)

3 2 1 0 −1 −2 −3

0

100

200 300 Sample

400 0

(a)

100

200 300 Sample

400 0

100

(b)

200 300 Sample

400

(c)

FIGURE 8.5 Phase samples measured on a 10-Gb/s BPSK signal. The phase is shown (a) without any processing, (b) after removal of a linear term leading to rotation in complex plane, and (c) after removal of both a linear term and a slowly-varying phase.

constant. The third term is a slowly-varying function of the sample number. Since the differential phase levels are discrete, the samples of Equation (8.25) corresponding to a single level chosen arbitrarily can be isolated. The evolution of the phase of the isolated subset can be tracked using a slowly-varying function, which leads to the function O(tnþ1 – tn) þ y(tnþ1) – y(tn). This determined phase function can be subtracted from the differential phase before concatenation, which leads to the estimated phase cDATA(tn). If the modulation format is known and consists of M levels equally spaced in the interval [0, 2p] (e.g., M ¼ 2 for BPSK and M ¼ 4 for QPSK, with each level corresponding to an Mth root of 1), the rotation and slowly-varying phase drifts can be removed by considering the quantity M arg[S(tn)], which is the phase of [S(tn)]M, written as M arg½Sðtn Þ ¼ McDATA ðtn Þ þ MOtn þ Myðtn Þ:

(8.26)

By definition, the phase modulation for ideal noiseless data verifies McDATA(tn) ¼ 0, so that Equation (8.26) can be lowpass filtered to obtain the phase drift between sources. (Note that noise on the measured data signal is assumed to be uncorrelated between samples and should not be filtered.) The phase drift can be subtracted from the data to obtain the phase of the sampled complex interference cDATA(nT). Techniques for phase tracking are described extensively in Reference 24. Phase tracking is made easier if the phase variations of the two sources are small during the acquisition time of a representative sample set. Because samples are acquired at the rate of the analog-to-digital converters in the case of a monochromatic LO and at the repetition rate of the sampling source when using a pulsed LO, phase tracking should generally be easier with monochromatic sources. As a general rule, it is preferable to operate with sources having a long coherence time (i.e., narrow linewidth for a monochromatic LO, or narrow spectral linewidth of each optical frequency of the spectral comb for a periodic sampling source) and sampling sources with a high repetition rate, all other things being kept equal.

8.3 IMPLEMENTATIONS OF LOS This section describes various experimental implementations and practical aspects of LOS.

206


8.3.1 Balanced photodetection If balanced photodetectors with adequate bandwidth are available, a 90-degree optical hybrid can be used. One practical advantage of balanced photodetection is that the noninterferometric terms (i.e., the intensities of each source) do not need to be sampled—that is, the entire dynamic range of the analog-to-digital conversion is used to sample the phase-sensitive signal. The 90-degree optical hybrid generally uses distinct optical paths as represented in Figure 8.2. These optical paths can be implemented with free-space propagation, optical fibers, or waveguide. Stability of the phase difference between different electric fields must be ensured during the acquisition of a representative set of samples. This can usually be ensured without active stabilization, even with optical fibers.16

8.3.1.1 Polarization 90-degree optical hybrid Polarization interferometers are commonly used in interferometric measurements and have been used for coherent detection.23,25 They use two orthogonal polarization modes to implement an environmentally stable interferometer. A polarizer with axes rotated by 45 degrees relative to the input states projects the two states on the two output ports of the polarizer, therefore acting like a beam splitter. A relative phase shift between two orthogonally polarized optical waves can be introduced by waveplates properly aligned. Advantageously, since two orthogonally polarized sources can propagate along the same optical path, phase fluctuations due to turbulence or thermal drift have little impact on the interference that occurs after projection along a common axis. Polarization states can be used in an optical hybrid, for example, with the following implementation. Let’s consider two orthogonal axes x and y and the combination of the local oscillator and data source with linear orthogonal polarization states: ˘

˘

˘

˘

˘

E ¼ ELO x þ EDATA y:

(8.27)

˘

˘

˘

This field is split into two identical fields E A ¼pEffiffiBffi ¼ p1ffiffi2 E with p a nonpolarizing beam splitter. Sendffiffiffi ing EA to a polarizer with axes along ð x þ yÞ= 2 and ð x yÞ= 2 leads to the fields 1 E A;1 ¼ ½ELO þ EDATA ð x þ yÞ; (8.28) 2 and 1 E A;2 ¼ ½ELO EDATA ð x yÞ: (8.29) 2 ˘

˘

˘

˘

˘

˘

˘

˘

˘

˘ ˘

Balanced square-law photodetection of these two fields gives SA ¼ Re½EDATA ELO :

(8.30)

˘

EB propagates into a quarter waveplate aligned to induce a relative p/2 phase shift between the two orthogonally polarized fields. After the waveplate, one has 1 E B ¼ pffiffiffi ½iELO x þ EDATA y: (8.31) 2 pffiffiffi pffiffiffi Balanced photodetection after a polarizer oriented along ð x þ yÞ= 2 and ð x yÞ= 2 gives ˘

˘

˘

˘

˘

˘

SB ¼ Im½EDATA ELO :

(8.32)

˘


3-dB coupler

207

p /2 phase l /4

l /2

Data source Sampling source

l /2

Polarizer Balanced photodetector

FIGURE 8.6 Schematic of a polarization interferometer. The orthogonally polarized data and sampling sources are combined with a nonpolarizing 3-dB coupler. The two outputs of the coupler are sent to identical setups leading to a pair of balanced photodetectors, excepting that a p/2 phase shift is introduced in one arm, so that the real and imaginary parts of the interference are measured.

A schematic of a polarization interferometer achieving these operations is presented in Figure 8.6, based on an implementation of LOS previously published.23 The two input sources have orthogonal polarization states and are split and coupled by a nonpolarizing 3-dB coupler. The two outputs are independently processed to yield the two balanced photodetection signals. In the lower optical path, a 45-degree polarization rotation is performed so that a polarizer combines the two sources along its horizontal and vertical axes. In the upper path, a differential p/2 phase shift is added on one of the sources by propagation through a quarter-wave plate with axes horizontal and vertical. A 45-degree polarization rotation is then performed by a half-wave plate, and balanced photodetection is performed on the two outputs of a polarizer. Since operations on polarization states can be performed easily by combinations of birefringent elements, polarization interferometers suitable for coherent sampling can be integrated.

8.3.1.2 Waveguide 90-degree optical hybrid Waveguide structures are attractive for compact stable implementations of optical hybrids. In the 1.5- and 1.3-micron telecommunication regions, waveguides with low linear losses can be fabricated using, for example, Silicon-on-Insulator. A 90-degree waveguide optical hybrid (Figure 8.7(a)) exactly replicates the splitting and recombining shown in Figure 8.2.18 The waveguide optical hybrid has been used in a LOS setup to sample a data source with a copolarized free-running mode-locked laser (Figure 8.7(b)). Each source is independently split and recombined, and the sampling signals are measured by two pairs of balanced photodetectors. The repetition rate of the sampling laser was 10 MHz, and the bandwidth of the photodetectors was 800 MHz. The broadband mode–locked laser (40 nm) was spectrally filtered to match the spectrum of the data source, typically with a 3-nm filter. The analog-to-digital conversion of the photodetector signals is synchronized with the sampling events by sending part of the input sampling pulse onto a photodiode. Since the optical path length differences between splitting and recombining of the different sources are smaller than the temporal duration/resolution, it is necessary and sufficient to control the optical phase of one of the split sources to get the real and imaginary part of the same complex interference. This can

208


Sampling source Data source

Pulse generator

Hybrid Filter

SA

BDA

PC

A/D

SB

BDB V

(a)

(b)

FIGURE 8.7 (a) Picture of a 90-degree optical hybrid made with silicon-on-silica. (b) Setup for coherent photodetection of a data source with a copolarized pulsed local oscillator using the waveguide 90-degree optical hybrid. The sampling source is spectrally filtered to match the optical spectrum of the data source. The relative phase between the two measured signals is controlled by applying a low continuous voltage to a thermo-optic coupler.18 Copyright © 2005 OSA.

be achieved using the electro-optic effect (change of index induced by a voltage) or by a thermooptic effect (change of index controlled by the temperature). In the latter case, a thermo-optic coupler is driven by a control voltage, so that a change in voltage effectively controls the relative phase. This implementation was used to characterize various phase-encoded sources at 10 Gb/s and 40 Gb/s, as well as OOK optical sources at rates as high as 640 Gb/s.

8.3.2 Direct photodetection If balanced photodetectors are not available, only two ports of a 90-degree optical hybrid need to be used. In this case, a simpler structure where the data source is, for example, circularly polarized while the sampffiffiffi 21 pling source is linearly pffiffiffi polarized can be used. With the input field EðtÞ ¼ ELO ðtÞð x þ i yÞ= 2 þEDATA ðtÞð x þ yÞ= 2, the fields at the two outputs of a polarizing beam splitter with axes x and y are ˘ ˘

˘

˘

˘

˘

SA;1 ¼

1 1 1 j ELO þ EDATA j2 ¼ j ELO j2 þ j EDATA j2 þ Re½EDATA ELO ; 2 2 2

(8.33)

SB;1 ¼

1 1 1 j iELO þ EDATA j2 ¼ j ELO j2 þ j EDATA j2 þ Im½EDATA ELO : 2 2 2

(8.34)

˘

and

Therefore, the real and imaginary part of the interference can be obtained by considering a large collection of samples and removing the average value of the two measured signals, following Equation (8.10). In practice, this removal can be performed by AC-coupled photodetectors.

8.3.3 LOS with four-wave mixing Coherent detection of a data-encoded signal can be performed with an independent optical gate and local oscillator, provided that the optical gating mechanism preserves the phase information—that is, it is possible to retrieve the data phase from the measured phase of the gated signal. This was implemented using four-wave mixing in a highly nonlinear fiber, a process that can be phase matched to provide high temporal resolution and large spectral coverage.21,26 As described previously, the phase


209

lpump = 1547 nm 1.5625 GHz TFWHM = 4.5 ps Sampling pulse source

LO PC

HNLF

PC PBS

2 nm

Data source L = 50 m = 10 W–1km–1 idler = 1530 nm

PC

ADC 8 bits ADC 8 bits

DSP (offline)

FIGURE 8.8 Setup for the coherent photodetection of a data source by gating with four-wave mixing and detection with a monochromatic local oscillator. The sampling and data source are combined and propagate in a highly nonlinear fiber. The idler resulting from four-wave mixing of the sampling source acting as a pump on the data source is filtered and detected by homodyne detection with a local oscillator.21 Courtesy of Mathias Westlund and Peter A. Andrekson; copyright © 2009 OSA.

of the generated idler is related to the phase of the data by cIDLER(t) ¼ 2cPUMP(t) – cDATA(t). This phase can be measured by coherent photodetection with a monochromatic local oscillator having an optical frequency close to oIDLER ¼ 2oPUMP – oDATA. A suitable setup is presented in Figure 8.8. The copolarized data and sampling source are combined and propagate in a highly nonlinear fiber at a power sufficient to generate a measurable idler. The sampling rate is set by the repetition rate of the sampling source (1.5 GSamples/s). The idler is spectrally filtered and is detected by coherent photodetection with a monochromatic laser and two photodetectors. The linearly polarized idler and circularly polarized local oscillator are combined and sent to a polarizing beam splitter, and direct photodetection is performed on each output port of the splitter to obtain the real and imaginary part of the interference signal. Software synchronization was performed to yield samples of the electric field of the data source over the complete bit sequence of a PRBS signal. Examples of experimental results are presented in Figure 8.9 for a 10-GBaud QPSK signal. The constellation diagram clearly shows the four symbols separated by p/2. These four levels are also clearly visible on the plots of the measured phase as a function of the location in the bit sequence, which were obtained by software synchronization.

8.3.4 Correction of imperfections 8.3.4.1 Quadrature amplitude correction When the data and sampling sources are not mutually coherent, the phase variations between the two sources allow correction of experimental imperfections in the optical setup or photodetectors. For the signals SA and SB, one can show that hSA i ¼ hSB i ¼ 0;

(8.35)

hS2A i ¼ hS2B i:

(8.36)

and

210


Optical phase

p 100 ps p /2 0 –p/2 Time

Optical phase

(a)

(c)

p p /2 0 –p/2 0

Time

27 − 1 bits

(b)

FIGURE 8.9 Experimental results obtained with four-wave mixing and coherent detection. (a) Constellation diagram of 625,000 samples measured on a 10-GBaud QPSK signal. Electric-field samples located at the center of the bit slots in a time interval corresponding to 20% of the bit period are plotted in black. Other samples appear in gray, while lines correspond to interbit transitions averaged over a large number of similar transitions in the bit sequence. (b) Measured phase as function of position in the bit sequence. (c) Close-up of (b) in a 10-bit intervals.21 Courtesy of Mathias Westlund and Peter A. Andrekson; copyright © 2009 OSA.

A large collection of samples {SA} and {SB} measured over a time interval much longer than the mutual coherence time of the two sources can therefore be corrected by removal of the DC part of each collection, therefore ensuring hSAi ¼ hSBi ¼ 0, and normalization by its standard deviation, therefore ensuring hS2A i ¼ hS2B i.

8.3.4.2 Quadrature phase correction For two mutually incoherent sources, the relative phase shift D’AB between the two signals SA and SB verifies cosðD’AB Þ ¼

2hSA SB i : S2A þ S2B

(8.37)

This can be used experimentally to tune the differential phase between the measured signals SA and SB and set it to its optimal value p/2, or numerically correct imperfections.18,27 The sampled complex

8.4 Optical performance monitoring with LOS

211

signal can always be written as the sum of the two measured signals as long as their relative angle is known and not a multiple of p. If the adjustment of the relative phase is not experimentally possible, the complex signal z can be calculated using the measured signals, which are SA ¼ Re(z) and SB ¼ Re[z exp(iD’AB)], to obtain z ¼ ReðzÞ þ i ImðzÞ SB cosðD’AB ÞSA ¼ SA þ i : sinðD’AB Þ

(8.38)

This should be restricted to large values of sin(D’AB) (i.e., D’AB close to (p/2), since noise on the measured signals strongly impacts the noise on the measured complex interference for small values of this quantity.

8.4 OPTICAL PERFORMANCE MONITORING WITH LOS This section presents various examples of optical performance monitoring performed with coherent detection.

8.4.1 Characterization of amplified spontaneous emission Amplified spontaneous emission (ASE) is common in optical telecommunication systems since all amplifiers have a finite signal-to-noise ratio. ASE is not correlated to the (noise-free) signal, and the electric field of the noisy signal is ESIGNAL ðtÞ ¼ EDATA ðtÞ þ EASE ðtÞ;

(8.39)

where the phase of EASE is uniformly distributed in the interval [0,2p] and the modulus of EASE has a normal distribution. The symbols of the noisy signal are therefore isotropically spread in the complex plane. In Figure 8.10(a), the constellation diagram of an ASE source is plotted, in which case EDATA(t) ¼ 0. In Figure 8.10(b), the constellation diagram of a continuous-wave (CW) source coupled with an ASE source is plotted. In Figures 8.10(c) and (d), the constellation diagrams of a BPSK signal with two different OSNRs are plotted. As the OSNR is decreased, the spread of the symbols increases in the complex plane. Noting that both the variance of the noise on each symbol and the OSNR are proportional to a bilinear function of the electric field of the ASE, a linear relation between these two quantities is expected, and can indeed be derived. Figure 8.10(e) demonstrates the link between variance of the sampled electric field of a given symbol along the real and imaginary axis and a linear function of the OSNR. A good agreement is obtained down to an OSNR of the order of 25 dB in this particular implementation.

8.4.2 Phase and amplitude noise measurements From LOS data, the amplitude and phase noise on a symbol can be obtained simply by considering the amplitude and phase spread of the symbols in the complex plane. Exemplary constellation diagrams of phase-encoded sources are shown in Figure 8.11. These phase-encoded signals were generated by directly driving a LiNbO3 phase modulator with a two-level PRBS drive. Tuning the

212


(a)

(b)

(c)

(d)

1

sx 2 sy 2

Variance

10–1

Theory 10–2 10–3 10–4

5

10

15

20

25

30

35

40

45

OSNR (dB) (e)

FIGURE 8.10 Constellation diagrams measured (a) on an ASE source, (b) on a monochromatic source with an OSNR of 15.8 dB, (c) on a binary PSK signal with an OSNR of 18 dB, and (d) on a binary PSK signal with an OSNR of 13 dB. (e) Measured variance of one of the symbols along the real and imaginary axes s2x and s2y versus the measured OSNR. Line of dots represents the theoretical relation between variance and OSNR.19 Copyright © 2006 OSA.

voltage difference between the two levels changes the relative optical phase of the two levels and increases the noise on each level, as can be observed in Figures 8.11(a) and (b). However, since only phase modulation is performed, the amplitude noise does not depend on the amount of phase modulation. Figure 8.11(c) presents a quantification of the phase and amplitude noise as a function of the phase difference between the two encoded levels: the phase noise increases linearly while the amplitude noise is constant, as expected. In practice, this approach to the generation of phase-encoded signals is avoided because of the inherent phase noise induced by the noise on the drive voltage. It is preferred to use a Mach-Zehnder modulator and benefit from the sign change of the transfer function when going through extinction. This provides a phase shift exactly equal to p regardless of the modulation amplitude, and amplitude noise can be minimized by proper tuning of the amplitude of the modulation. These properties are demonstrated in Figure 8.12. The two constellation diagrams plotted in Figures 8.12(a) and (b) were measured for AC drive voltages with different amplitudes, the bias of the Mach-Zehnder modulator being set for extinction. A p phase shift between levels is obtained regardless of the drive amplitude, and the phase noise does not depend significantly on the drive amplitude. However, the amplitude noise decreases as the drive amplitude is increased,


(b)

0.3

0.3

0.2

0.2

0.1

0.1

sy

sr

(a)

213

0

0 0

1

2 Phase (rad) (c)

3

4

FIGURE 8.11 (a, b) Constellation diagrams of phase-modulated signals generated with a phase modulator for differential phase of p/2 and p. (c) Standard deviation of the amplitude sr and phase sc of one of the symbols as a function of the differential phase (respectively round markers and square markers).19 Copyright © 2006 OSA.

as the sinusoidal transfer function of the modulator clamps the amplitude modulation when the voltage amplitude is sufficient (Figure 8.12(c)). The direct characterization of the amplitude and phase noise is important in many aspects of optical telecommunications. Taking as an example the BPSK regeneration experiment described in Reference 28, the wavelength-conversion process modifies the amplitude and phase noise properties of the signal. A performance assessment of the channel in terms of bit error rate only leads to an indirect understanding of the properties of the converter. Figure 8.13 demonstrates the phaseregeneration capability of the proposed setup when operating on signals with phase noise. The constellation diagrams measured on the input signal (Figures 8.13(a) and (c)) and on the converted signal (Figures 8.13(b) and (d)) demonstrate a reduction of the phase noise, which could lead to an appreciable transmission performance improvement. The standard deviation of the phase is reduced from 0.3 rad to 0.14 rad after conversion (Figures 8.13(c) and (d)).

8.4.3 Nonlinear phase noise Because of the intensity dependence of the index of materials, optical waves propagating in long distances of optical fibers accumulate a nonlinear phase shift. The effect of this phase shift depends on the modulation format, power into the fiber spans, chromatic dispersion of the fiber spans, and more. In general, the propagation of a source with instantaneous power p(t) generates an optical phase

214


(b)

0.15

0.15

0.1

0.1

0.05

0.05

sy

sr

(a)

0 0

2

4

6

8

0 10

Voltage (V) (c)

FIGURE 8.12 (a, b) Constellation diagrams of BPSK signal generated with Mach-Zehnder modulator for two different amplitudes of the drive voltage. (c) Standard deviation of the amplitude and phase of one of the symbols as a function of the drive voltage (respectively round markers and square markers).19 Copyright © 2006 OSA.

(a)

(b)

(c)

(d)

FIGURE 8.13 Constellation diagrams measured at input and output of wavelength converter set to reduce the phase noise of a BPSK signal. (a) and (c) are measured before the wavelength converter, while (b) and (d) are the corresponding converted signals.28 Copyright © 2008 IEEE.

equal to gLp(t) by self-phase modulation in a medium of length L and nonlinear coefficient g. This process therefore couples the temporal phase and amplitude of the electric field. The ASE-signal beat noise of a source with finite OSNR leads to random amplitude fluctuations of the field, which induce nonlinear phase noise during nonlinear propagation (Gordon-Mollenauer phase noise).29 An example of the BPSK constellation diagram with such noise is displayed in Figure 8.14. Figure 8.14(a)


(a)

215

(b)

0.4

C (rad)

0.3 0.2 0.1 0 0

10

20 30 Power (mW)

40

50

(c)

FIGURE 8.14 Constellation diagrams measured after propagation of a noisy signal in a highly nonlinear fiber at (a) low power and (b) high power. The coupling between the intensity and phase of the samples is a sign of GordonMollenauer phase noise. This coupling is quantified in (c) as a function of the average power of the source.19 Copyright © 2006 OSA.

represents the constellation diagram measured after propagation in a nonlinear fiber at low power, and the reduced OSNR is indicated by the isotropic spread of the symbols. Figure 8.14(b) represents the constellation diagram measured after propagation in the same fiber at a power sufficient to induce significant nonlinearities. A clear coupling between phase and amplitude of the samples measured for each symbol is observed. The coupling between intensity and phase of the samples can be quantified with the measured constellation diagrams. For example, one can define C as the slope of the fit of the measured phase versus the measured intensity for a collection of samples (the intensity being normalized to an average value equal to one). As can be seen in Figure 8.14(c), the coupling increases as the average input power into the fiber increases. This is expected from the linear relation between induced nonlinear phase and power in the nonlinear medium. Figure 8.15 demonstrates the use of LOS with a short local oscillator on a 20-Gb/s QPSK signal propagating in a recirculating loop. The constellation diagrams of the transmitted signal were measured for different input average power and number of roundtrips in the loop (i.e., propagation distance in the transmission fiber). A typical constellation diagram after propagation is displayed in Figure 8.15(a), showing a clear coupling between intensity and phase of the four symbols of the QPSK signal. The amplitude of the coupling is expected to increase with the injected power and the transmission distance. This is confirmed by the plot presented in Figure 8.15(b), which

216


0.5

C (rad)

0.4 0.3 0.2

800 km 1200 km

0.1 0 0

1000 2000 3000 Power × distance (mW.km)

(a)

(b)

FIGURE 8.15 (a) Constellation diagram of a QPSK signal measured after two roundtrips in a recirculating loop (the propagation distance in transmission fiber is 800 km). Gordon-Mollenauer phase noise can be seen. (b) Coupling between intensity and phase in constellation diagrams measured for various propagation distances and/or launch powers.

shows the measured phase-intensity coupling as a function of the product of the input power and transmission distance. The data in this plot were obtained with seven constellation diagrams, measured for either two or three roundtrips in the recirculating loop (propagation distance of 800 km and 1200 km in transmission fiber, respectively). Direct detection and quantification of nonlinear phase noise is made possible by phase-sensitive sampling.

8.4.4 Nonlinear phase-shift measurement Because of its ability to measure both intensity and phase of an optical wave, LOS can directly measure the nonlinearities introduced by optical fibers and components. One way to accomplish this is by propagation of a modulated wave into a test fiber at different optical powers and comparison of the measured output temporal phase to the measured output temporal intensity.30 Figure 8.16 shows a comparison of the measured power and phase of a modulated signal after propagation in a nonlinear fiber. Self-phase modulation leads to proportionality of these two quantities, and the nonlinear coefficient of the fiber can be recovered after proper scaling.

8.4.5 Digital processing of sampled electric field When operated at a sufficiently high sampling rate, coherent photodetection leads to continuous representations of the electric field. Since a continuous set of samples of the complex temporal field is obtained, various distortions can be directly measured on data-encoded signals, and digital postdetection compensation can be performed.16,17,31 Figure 8.17 shows 10-Gb/s constellation diagrams obtained with LOS implemented with a monochromatic laser,32 demonstrating the effect of chromatic dispersion. The two symbols are visible in the complex plane, and the constellation diagrams before (Figure 8.17(a)) and after chromatic dispersion (Figure 8.17(b)) are clearly different. In particular, chromatic dispersion leads to separation of the bit centers along a circle in the complex plane


217

0.4

–0.4

1

Δj (rad)

Δp

0

0 –1 0

10

20

30 40 Sample

50

60

FIGURE 8.16

3

3

2

2

1

1 Im(E)

Im(E)

Measured relative instantaneous power and phase of an amplitude-modulated optical source after propagation in a nonlinear fiber. The coupling between power and phase is due to self-phase modulation, and the nonlinear coefficient of the fiber can be quantified with these data.30 Copyright © 2005, Institution of Engineering and Technology.

0 –1

–1 –2

–2 –3 –3 –2 –1

0

0 1 Re(E) (a)

2

3

–3 –3 –2 –1

0 1 Re(E)

2

3

(b)

FIGURE 8.17 Examples of measured constellation diagrams of a 10.7-Gb/s BPSK signal (a) after the transmitter, and (b) after propagation in 90 km of standard single-mode fiber. The trajectories of the electric field in the complex plane are plotted with continuous gray lines and the values of the field at the center of the bit slot are plotted with a black round marker. Courtesy of Michael G. Taylor.

with different associated trajectories. Postdetection digital processing of the sampled electric field can be used to compensate impairments; for example, chromatic dispersion can be compensated by convolution with the impulse response corresponding to exp(i’(2) o2/2), where ’(2) is the accumulated second-order dispersion.16

8.4.6 Characterization of the electric field of periodic sources LOS can also characterize periodic sources, in the context of optical telecommunications or more generally in ultrafast optics.33 Assuming a period TTEST for the test source and TSAMPLING for the sampling source, samples of the intensity and phase of the source can be measured at the repetition

218


1

4 1

1

Phase (rad)

2

Intensity (a.u.)

3

0

Phase (rad)

Transmission (a.u.)

1

0 0

0

20

40

60

–1 80 100

0 0

5

10

15

Time (ps)

Time (ps)

(a)

(b)

20

–1 25

FIGURE 8.18 (a) Temporal transmission and phase of a semiconductor optical amplifier depleted by a short optical pulse. (b) Temporal intensity and phase of an optical pulse carved by an electro-absorption modulator. In (a), the period of the depleting pulse is 100 ps, while in (b), the period of the drive voltage is 25 ps. The lines correspond to the quantities measured with linear optical sampling and the markers correspond to the same quantities measured with the spectrogram technique.33 Copyright © 2005 OSA.

rate of the sampling laser—that is, every TSAMPLING. The samples can be assembled into a representation of the electric field of the test source in one single period by numerical resynchronization. Defining dt ¼ TSAMPLING – nTTEST, the collection of measured samples can be plotted with an axis having a sampling rate dt. Measurement of the amplitude of the correlation between two periodic laser sources has also been performed with two photodiodes.34 Figure 8.18 displays examples of electric field measurement in the context of pulse carving and device characterization. In Figure 8.18 (a), the transmission and phase of a semiconductor optical amplifier depleted by a short optical pulse are shown. The carrier depletion leads to a significant decrease of the transmission and a large induced phase. Recovery occurs with the transmission and phase reaching their predepletion levels. Figure 8.18(b) shows the intensity and phase of an optical pulse generated by carving a monochromatic laser with an electro-absorption modulator driven by a 40-GHz sinusoidal voltage. Phase modulation occurs at the same time as amplitude modulation, and it is of practical importance to understand the coupling between these two quantities. In these two cases, the results are in very good agreement with independent measurements performed with the linear spectrogram technique.8

8.5 RECENT RESULTS AND RELATED TECHNIQUES Various techniques based on coherent detection have been recently demonstrated. Two optical hybrids have been used to characterize differentially encoded signals by sampling two successive bits of the data stream. This is achieved by adding a relative delay equal to the bit period on the sampling source going into one of the hybrids, so that the difference between the phases measured by the first and second sampling system is essentially the phase difference between two successive bits.35,36 Because two phase samples separated by a small delay are compared, the requirement on the coherence time of the interfering sources is decreased, but only differential measurements

Acknowledgments

219

of the phase are obtained. An impairment such as nonlinear phase noise is clearly visible on samples of the electric field of the data source, but might not be as clearly identifiable with the differential phase. If the delay between the two sampling systems is decreased, the measured phase difference scales like the instantaneous frequency around the sampled times.27 A configuration with two optical hybrids has also been used to characterize optical devices.37 In that demonstration, one of the hybrids is used to provide a phase reference signal on a monochromatic laser, while the other hybrid measures the same laser after phase modulation. The two signals are essentially sampled by the same sampling laser at the same time, so that the fluctuating phases of the monochromatic laser and sampling laser exactly cancel out in the differential phase. This provides a measurement of the actual phase introduced, for example, by a modulator without a strong requirement on the coherence of the sampling source and monochromatic laser. High-resolution, high-precision measurements of optical components have also been obtained using coherent detection of two phase-locked femtosecond oscillators.38 Polarization diversity implementations of linear sampling have been demonstrated.39–41 In one of these implementations (Reference 41), sampling of two combined channels was performed, and the intensity of the independent channels was obtained by digital processing. A precision time base has also been used for word-synchronous sampling.42 This allowed measurement and averaging of various bit patterns. Software synchronization has also been used with some implementations of coherent sampling.21 Finally, direct coherent detection has seen impressive developments thanks partly to signal processing techniques that allow direct impairment compensation using digital processing of the electric field.31 The associated phase-tracking algorithms can be used for LOS. Optical components such as 90-degree optical hybrids are now widely commercially available.

8.6 SUMMARY Coherent homodyne detection can be used as a high-resolution temporal diagnostic for optical sources. This capability is unique and advantageous for optical performance monitoring. Valuable assessment of the performance of telecommunication systems and devices is obtained with statistical measurements of the electric field of the optical signals. Because the technology of optical telecommunications is constantly evolving, diagnostics are evolving accordingly. Since coherent detection has seen increased interest over the past few years for optical transmission, diagnostics providing relevant information on the intensity and phase of optical signals naturally fit into this evolution.

ACKNOWLEDGMENTS This work was partially supported by the U.S. Department of Energy Office of Inertial Confinement Fusion under Cooperative Agreement No. DE-FC52-08NA28302, the University of Rochester, and the New York State Energy Research and Development Authority. The support of DOE does not constitute an endorsement by DOE of the views expressed in this article. Some of the concepts and experiments presented in this chapter were developed by the author while working at Bell Laboratories (Lucent Technologies). The author is thankful to many of his former colleagues for fruitful interactions, in particular Christopher Doerr, Inuk Kang, Daniel Kilper, Howard Stuart, and

220


Peter Winzer (all from Bell Laboratories), as well as Michael Raymer (Oregon Center for Optics, University of Oregon). Fruitful discussions with Mathias Westlund (Chalmers University of Technology), Peter Andrekson (Chalmers University of Technology), and Michael Taylor (Atlantic Sciences) during the preparation of this chapter are also acknowledged.

REFERENCES 1. Ip E, Lau AP, Barros DJF, Kahn JM. Coherent detection in optical fiber systems. Opt Express 2008;16:753–91. 2. Ho KP. Phase-modulated optical communication systems. Berlin: Springer; 2005. 3. Dinu M, Kilper DC, Stuart HR. Optical performance monitoring using data stream intensity autocorrelation. J Lightwave Technol 2006;24:1194–202. 4. Dorrer C. High-speed measurements for optical telecommunication systems. IEEE J Sel Top Quantum Electron 2006;12:843–58. 5. Walmsley IA, Dorrer C. Characterization of ultrashort electromagnetic pulses. Adv Opt Photon 2009;1:308–437. 6. Trebino R. Frequency resolved optical gating: the measurement of ultrashort optical pulses. New York: Kluwer Academic Publishers; 2002. 7. Iaconis C, Walmsley IA. Spectral phase interferometry for direct electric-field reconstruction of ultrashort optical pulses. Opt Lett 1998;23:792–4. 8. Dorrer C, Kang I. Simultaneous temporal characterization of telecommunication optical pulses and modulators using spectrograms. Opt Lett 2002;27:1315–7. 9. Dorrer C, Kang I. Highly sensitive direct femtosecond pulse characterization using electro-optic spectral shearing interferometry. Opt Lett 2003;28:477–9. 10. Bennett CV, Scott RP, Kolner BH. Temporal magnification and reversal of 100 Gb/s optical data with an up-conversion time microscope. Appl Phys Lett 1994;65:2513–5. 11. Salem R, Foster MA, Turner-Foster AC, Geraghty DF, Lipson M, Gaeta AL. High-speed optical sampling using a silicon-chip temporal magnifier. Opt Express 2009;17:4324–9. 12. Dorrer C. Single-shot measurement of the electric field of optical waveforms by use of time magnification and heterodyning. Opt Lett 2006;30:3237–9. 13. Schmidt-Langhorst C, Weber H-G. Optical sampling techniques. Ultra-high-speed optical transmission technology. Berlin: Springer; 2007. 14. Cundiff ST, Ye J. Phase stabilization of mode-locked lasers. J Mod Opt 2005;52:201–19. 15. Walmsley IA, Waxer L, Dorrer C. The role of dispersion in ultrafast optics. Rev Sci Instrum 2001;72:1–29. 16. Taylor MG. Coherent detection method using DSP for demodulation of signal and subsequent equalization of propagation impairments. IEEE Photon Technol Lett 2004;16:674–6. 17. Ly-Gagnon D-S, Tsukamoto S, Katoh K, Kikuchi K. Coherent detection of optical quadrature phase-shift keying signals with carrier phase estimation. J Lightwave Technol 2006;24:12–21. 18. Dorrer C, Doerr CR, Kang I, Ryf R, Leuthold J, Winzer P. Measurement of eye diagrams and constellation diagrams of optical sources using linear optics and waveguide technology. J Lightwave Technol 2005;23:178–86. 19. Dorrer C. Monitoring of optical signals from constellation diagrams measured with linear optical sampling. J Lightwave Technol 2006;24:313–21. 20. Kim I, Kim C, Li G. Requirements for the sampling source in coherent linear sampling. Opt Express 2004;12:2723–30.

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21. Sko¨ld M, Westlund M, Sunnerud H, Andrekson PA. All-optical waveform sampling in high-speed optical communication systems using advanced modulation formats. J Lightwave Technol 2009;27:3662–71. 22. Ito F. Demultiplexed detection of ultrafast optical signal using interferometric cross-correlation technique. J Lightwave Technol 1997;15:930–7. 23. Dorrer C, Kilper DC, Stuart HR, Raybon G, Raymer MG. Linear optical sampling. IEEE Photon Technol Lett 2003;15:1746–8. 24. Taylor MG. Phase estimation methods for optical coherent detection using digital signal processing. J Lightwave Technol 2009;27:901–14. 25. Leeb WR. Optical 90 hybrid for Costas-type receivers. Electron Lett 1990;26:1431–2. 26. Westlund M, Andrekson PA, Sunnerud H, Hansryd J, Li J. High-performance optical-fiber-nonlinearitybased optical waveform monitoring. J Lightwave Technol 2005;23:2012–22. 27. Okamoto K, Ito F. Dual-channel linear optical sampling for simultaneously monitoring ultrafast intensity and phase modulation. J Lightwave Technol 2009;27:2169–75. 28. Kang I, Dorrer C, Zhang L, Dinu M, Rasras M, Buhl LL, et al. Characterization of the dynamical processes in all-optical signal processing using semiconductor optical amplifiers. IEEE J Sel Top Quantum Electron 2008;14:758–69. 29. Gordon JP, Mollenauer LF. Phase noise in photonic communications systems using linear amplifiers. Opt Lett 1990;15:1351–3. 30. Dorrer C. Direct measurement of nonlinear coefficient of optical fiber using linear optical sampling. Electron Lett 2005;41:8–10. 31. Li G. Recent advances in coherent optical communication. Adv Opt Photon 2009;1:279–307. 32. Taylor MG. Measurement of phase diagrams of optical communication signals using sampled coherent detection. In: Proc. symposium on optical fiber measurements. Boulder, CO; 2004. p. 163–4. 33. Dorrer C. Complete characterization of periodic optical sources by use of sampled test-plus-reference interferometry. Opt Lett 2005;30:2022–4. 34. Benkler E, Telle HR, Weingarten K, Krainer L, Spu¨hler G, Keller U. Characterization of ultrashort optical pulse properties by amplitude-modulation-balanced heterodyne gating. Opt Lett 2005;30:2016–8. 35. Okamoto K, Ito F. Ultrafast measurement of optical DPSK signals using 1-symbol delayed dual-channel linear optical sampling. IEEE Photon Technol Lett 2008;20:948–50. 36. Okamoto K, Ito F. Nearly shot-noise-limited performance of dual-channel linear optical sampling for ultrafast DPSK signals. IEEE J Quantum Electron 2009;45:711–9. 37. Williams PA, Dennis T, Coddington I, Swann WC, Newbury N. Vector signal characterization of high-speed optical components by use of linear optical sampling with milliradian resolution. IEEE Photon Technol Lett 2008;20:2007–9. 38. Coddington I, Swann WC, Newbury N. Coherent linear optical sampling at 15 bits of resolution. Opt Lett 2009;34:2153–5. 39. Okamoto K, Fan X, Ito F. Ultrafast sampling of complex polarization components for characterizing polarization mode dispersion. In: Proc. optical fiber communication conference, paper OTuN6. Anaheim, California; 2007. 40. Williams PA, Dennis T, Coddington I, Newbury N. Polarization-sensitive linear optical sampling for characterization of NRZ polarization-multiplexed QPSK. In: Proc. optical fiber communication conference, paper OThH2. San Diego, California; 2009. 41. Okamoto K, Ito F. Simultaneous WDM signal detection realized by ultrafast field sampling. Opt Express 2009;17:6696–702. 42. Dennis T, Williams PA, Coddington I, Newbury N. Word-synchronous linear optical sampling of 40 Gb/s QPSK signals. In: Proc. optical fiber communication conference, paper OThH3. San Diego, California; 2009.


CHAPTER

Optical performance monitoring based on RF pilot tones

9

Paul K.J. Park*, Yun C. Chung{ *Samsung Advanced Institute of Technology, Samsung Electronics, Korea Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Korea

{

9.1 INTRODUCTION For the proper operation and maintenance of modern dynamic wavelength-division-multiplexed (WDM) networks, it is essential to directly monitor the signal’s quality in the optical layer.1 For this purpose, there have been many efforts to utilize pilot tones, which represent small sinusoidal components added to WDM signals.2–11 For example, it has been reported that pilot tones can be used for monitoring various optical parameters of WDM signals such as optical channel power, wavelength, crosstalk, and so on. Unlike other optical performance monitoring techniques, the pilot-tone-based techniques can monitor these parameters without using the expensive demultiplexing filters such as tunable optical filters and diffraction gratings. Thus, this technique can be extremely cost effective. In addition, this technique is well suited for use in a dynamic WDM network, since the pilot tones are bound to follow their corresponding optical signals anywhere in the network. Therefore, the optical path of each WDM signal can be monitored simply by tracking its tone frequency. Although the pilot-tone-based monitoring technique has many advantages, it also has some limitations (particularly when the pilot tone is obtained simply by dithering the bias current of the transmitter laser). First, the pilot tone can impose unwanted amplitude modulation on the data signal and degrade receiver sensitivity.12 Second, the performance of the pilot-tone-based monitoring technique can be deteriorated by the “ghost tones” caused by the crossgain modulation (XGM) and stimulated Raman scattering (SRS).13 These problems can be mitigated by utilizing proper amplitudes and frequencies of pilot tones.2 However, for the use in the long-haul network with a large number of channels, it may still be necessary to restrict the number of WDM channels to be monitored simultaneously (e.g., by using an optical bandpass filter). Pilot tone can also be used for monitoring the chromatic dispersion (CD) and polarization mode dispersion (PMD) for adaptive compensators.8–11 These techniques typically utilize high-frequency (>1 GHz) pilot tones. However, the main drawback of these techniques is the difficulty in separating their effects, since the magnitude of such high-frequency pilot tone is dependent on both types of dispersions.2 In order to solve this problem, the phase-modulated (PM) pilot tones are used for monitoring CD instead of the conventional amplitude-modulated (AM) pilot tones.10 In contrast, the use of singlesideband (SSB) pilot tones has been proposed for monitoring the PMD without the effect of CD.2 © 2010 Elsevier Inc. All rights reserved. Doi: 10.1016/B978-0-12-374950-5.00009-2

223

224

CHAPTER 9 Optical performance monitoring based on RF pilot tones

In this chapter, we review various pilot-tone-based monitoring techniques proposed for the proper operation and maintenance of high-capacity WDM networks. In Section 9.2, we describe the operating principle of the pilot-tone-based monitoring technique and estimate its scalability. We also introduce the technique’s typical applications and implementation examples in this section. In Section 9.3, we present the recently proposed monitoring techniques based on the PM and frequencymodulated (FM) pilot tones. In Section 9.4, we review the pilot-tone-based monitoring techniques for CD and PMD used in adaptive compensators. We first review the monitoring techniques for CD and compare their performances by considering the effects of self-phase modulation (SPM) and PMD. In addition, we described the recent advancements in CD monitoring techniques obtained by utilizing the chirped pilot tone and the AM pilot tone carried by a broadband light source. We also review the PMD monitoring technique that is based on the SSB pilot tones and discuss its performance in the presence of CD. Finally, the chapter is summarized in Section 9.5.

9.2 PERFORMANCE MONITORING TECHNIQUES USING AM PILOT TONES In this section we first describe the operating principle of the pilot-tone-based monitoring technique and discuss its potential problems and possible solutions. Then, we estimate the scalability of the pilot-tone-based monitoring techniques and determine the proper range of tone frequencies for the targeted network. In addition, we review the techniques for monitoring various optical parameters of WDM signals such as optical power, wavelength, optical path, and crosstalk. We close this section with some implementation examples.

9.2.1 Operating principle Figure 9.1 shows that an optical signal is generated from node A and then transmitted to node C via node B. A pilot tone (i.e., a small sinusoidal component) is added to the optical signal at node A. At node B, this pilot tone can be extracted by using a low-speed photodiode and used for optical performance monitoring. It should be noted that in a dynamically configurable WDM network, pilot tones can also be used to monitor the optical paths of a WDM signal. This is because the pilot tone is bound to follow the corresponding WDM signal to wherever in the network once the tone is attached. Thus, we can monitor the optical path of each WDM signal only by tracking the tone frequency.

Pilot tone

Node A

Pilot tone Node B

Signal

Node C

FIGURE 9.1 Pilot-tone-based optical performance monitoring technique.2

9.2 Performance monitoring techniques using AM pilot tones

AM

LD

AM

LD

225

PM

LD

Bias

Bias

Data

Bias

Bias Data

(a)

(b)

(c)

PD

PD

A/D

BPF

FFT

RFD

RFD

(d)

(e)

(f)

PD LOSC

FIGURE 9.2 Pilot-tone generation and detection methods. (a) Adding a small sinusoidal current to the laser’s bias current. (b) Dithering bias voltage of external modulator. (c) PM tone generation by using phase modulator. (d) Pilottone detection using FFT. (e) Using tunable electrical bandpass filter. (f) Using tunable local oscillator for the down-conversion of tone frequency. LD, laser diode; AM, amplitude modulator; PM, phase modulator; PD, photodetector; A/D, analog-to-digital converter; FFT, fast Fourier transform; BPF, tunable bandpass filter; RFD, radio frequency power detector; LOSC, tunable local oscillator.2

For using the pilot tone in practical systems, it is necessary that pilot tones should be added into and extracted from WDM signals anywhere in the network. Figure 9.2 shows typical techniques used for the generation and detection of pilot tones.2 A pilot tone could be generated by dithering the laser’s bias current (Figure 9.2(a)),5 the bias voltage of the amplitude modulator (Figure 9.2(b)),9 or the phase modulator (Figure 9.2(c)).10 These techniques would require a slight modification of the existing transmitter and help in suppressing the stimulated Brillouin scattering.14 For the detection of pilot tones, a technique based on the fast Fourier transform (FFT) can be utilized as shown in Figure 9.2(d).5 This technique is attractive since every pilot tone added to WDM signals can be detected simultaneously without any scanning mechanism. In case of GHz tone frequency, a tunable electrical bandpass filter or a tunable local oscillator can be used for the tone detection, as shown in Figures 9.2(e) and (f), respectively.

9.2.2 Potential problems 9.2.2.1 Receiver sensitivity degradation When the AM pilot tone is added to an optical signal, it could interfere with data and cause deleterious effects.2 In earlier studies, it has been reported that these effects are dependent only on the modulation indices (MIs) and not on the frequencies of pilot tones. (MI is defined as the ratio between the peak amplitude of the pilot tone and the average signal amplitude.12) Thus, it would be required to maintain the MI within the acceptable level. However, because of the frequency response characteristics of the optical receiver, the maximum allowable MI is also dependent on


2.5

Receiver response

14 MI = 12 %

2.0

0.8 0.6

1.5

0.4 0.2

B.W. = 150 kHz

0.0

1.0

1

10

100

1000

MI = 6 % 0.5 0.0

12

1.0

MI of RF tone (%)

Power penalty (dB)

1.2

10 100 1000 Tone frequency (kHz) (a)

10

Data spectrum Fequency

8 6 4

Due to LPF characteristics of receiver

2

MI = 3 % 1

RF tone

Power

226

0

10000

1

2 3 4 5 6 7 8 9 10 Tone frequency (GHz) (b)

FIGURE 9.3 Effects of modulation index and frequency of pilot tone on 10-Gb/s NRZ signal (pattern length ¼ 231 1). (a) Pilot-tone-induced power penalty measured at low tone frequencies. (b) Maximum allowable modulation indices of high-frequency pilot tones for 0.5-dB penalty.2

the tone frequency. For example, Figure 9.3 shows the power penalties of a 10-Gb/s non-return-tozero (NRZ) signal (pattern length ¼ 2311) measured while varying the MI and the frequency of the pilot tone. The results show that the power penalty could be substantially reduced by using a tone frequency lower than 100 kHz. This was because the receiver used in the experiment had a lowfrequency cut-off at approximately 150 kHz. Thus, the power penalty could be maintained within 0.5 dB even when the MI was as high as 12%. However, the MI should be smaller than 6% when we used the tone frequency in the range of 1 MHz to 4.5 GHz. When the tone frequency was higher than 4.5 GHz, the pilot-tone-induced power penalty decreased because of the roll-off characteristics of the 10-Gb/s receiver. In order to reduce the tone-induced power penalty at a low tone frequency, it is possible to decrease the amplitude of a pilot tone by inserting a highpass filter into the optical receiver.15 However, if the highpass filter is used in the optical receiver, the waveform (i.e., “1” and “0” bits) of the data signal would be distorted as shown in Figure 9.4. In principle, this waveform can be distorted further as the number of consecutive 1- or 0-bits increases. When n consecutive 1-bits pass through the highpass filter in the optical receiver, the normalized amplitude of the nth bit at decision time x can be described as x ¼ e2pf0 ðn2Þ=B : 1

(9.1)

where f0 and B are the low cut-off frequency of the optical receiver and the bit rate, respectively. Similarly, in the case of n consecutive 0-bits, the normalized amplitude of the nth bit at the decision time, y, can be described as y ¼ 1 e2pf0 ðn2Þ=B ; 1

(9.2)

9.2 Performance monitoring techniques using AM pilot tones

227

Highpass filter “1”

“1”

“0”

“0”

“0”

Input data

“0”

Decision time

Decision time

1-bit

Output data

1-bit x

y 0-bit 0-bit

1/B

1/B

FIGURE 9.4 Effect of highpass filter on data signal.15

Equations (9.1) and (9.2) indicate that the highpass filter induces an eye closure of the data signal. Thus, the eye closure penalty, Pc, can be calculated as Pc ¼ 10 logðx yÞ:

(9.3)

Using these equations, we calculated the eye closure penalty while varying the low cut-off frequency in comparison with simulation results (using OptSim 3.0), as shown in Figure 9.5. In the calculation, we assumed that the bit rate and pattern length were 2.5 Gb/s and 271, respectively. The results 3.5

Eye closure penalty (dB)

3

Calculation Simulation

2.5 2 1.5 1 0.5 0 10

100 1000 10,000 Cut-off frequency (kHz)

100,000

FIGURE 9.5 Eye closure penalty calculated while varying low cut-off frequency in comparison with simulation results (bit rate ¼ 2.5 Gb/s, pattern length ¼ 27 1).15

228


show that the eye closure penalty increases with an increase in the low cut-off frequency of the optical receiver. However, the eye closure penalty is negligible (

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