Machine Learning for Future Fiber-Optic Communication Systems

Machine Learning for Future Fiber-Optic Communication Systems

1st Edition - February 10, 2022

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  • Editors: Alan Lau, Faisal Khan
  • Paperback ISBN: 9780323852272
  • eBook ISBN: 9780323852289

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Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting the state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve problems, and provide intelligent and efficient services to the end users. With up-to-date coverage and extensive treatment of various important topics related to machine learning for fiber-optic communication systems, this book is an invaluable reference for photonics researchers and engineers. It is also a very suitable text for graduate students interested in ML-based signal processing and networking.

Key Features

  • Discusses the reasons behind the recent popularity of machine learning (ML) concepts in modern optical communication networks and the why/where/how ML can play a unique role
  • Presents fundamental ML techniques like artificial neural networks (ANNs), support vector machines (SVMs), K-means clustering, expectation-maximization (EM) algorithm, principal component analysis (PCA), independent component analysis (ICA), reinforcement learning, and more
  • Covers advanced deep learning (DL) methods such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs)
  • Individual chapters focus on ML applications in key areas of optical communications and networking


R&D engineers in optical communications; University researchers in photonics

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • Contributors
  • Preface
  • Acknowledgments
  • Chapter One: Introduction to machine learning techniques: An optical communication's perspective
  • Abstract
  • 1.1. Introduction
  • 1.2. Supervised learning
  • 1.3. Unsupervised learning
  • 1.4. Reinforcement learning (RL)
  • 1.5. Deep learning techniques
  • 1.6. Future role of ML in optical communications
  • 1.7. Online resources for ML algorithms
  • 1.8. Conclusions
  • Appendix 1.A.
  • References
  • Chapter Two: Machine learning for long-haul optical systems
  • Abstract
  • 2.1. Introduction
  • 2.2. Application of machine learning in perturbation-based nonlinearity compensation
  • 2.3. Application of machine learning in digital backpropagation
  • 2.4. Outlook of machine learning in long-haul systems
  • References
  • Chapter Three: Machine learning for short reach optical fiber systems
  • Abstract
  • 3.1. Introduction to optical systems for short reach
  • 3.2. Deep learning approaches for digital signal processing
  • 3.3. Optical IM/DD systems based on deep learning
  • 3.4. Implementation on a transmission link
  • 3.5. Outlook
  • References
  • Chapter Four: Machine learning techniques for passive optical networks
  • Abstract
  • 4.1. Background
  • 4.2. The validation of NN effectiveness
  • 4.3. NN for nonlinear equalization
  • 4.4. End to end deep learning for optimal equalization
  • 4.5. FPGA implementation of NN equalizer
  • 4.6. Conclusions and perspectives
  • References
  • Chapter Five: End-to-end learning for fiber-optic communication systems
  • Abstract
  • Acknowledgements
  • 5.1. Introduction
  • 5.2. End-to-end learning
  • 5.3. End-to-end learning for fiber-optic communication systems
  • 5.4. Gradient-free end-to-end learning
  • 5.5. Conclusion
  • References
  • Chapter Six: Deep learning techniques for optical monitoring
  • Abstract
  • Acknowledgement
  • 6.1. Introduction
  • 6.2. Building blocks of deep learning-based optical monitors
  • 6.3. Deep learning-based optical monitors
  • 6.4. Tips for designing DNNs for DL-based optical monitoring
  • 6.5. Experimental verifications
  • 6.6. Future direction of data-analytic-based optical monitoring
  • 6.7. Summary
  • References
  • Chapter Seven: Machine Learning methods for Quality-of-Transmission estimation
  • Abstract
  • 7.1. Introduction
  • 7.2. Classification and regression models for QoT estimation
  • 7.3. Active and transfer learning approaches for QoT estimation
  • 7.4. On the integration of ML in optimization tools
  • 7.5. Illustrative numerical results
  • 7.6. Future research directions and challenges
  • 7.7. Conclusion
  • References
  • Chapter Eight: Machine Learning for optical spectrum analysis
  • Abstract
  • List of acronyms
  • 8.1. Introduction
  • 8.2. Feature-based spectrum monitoring
  • 8.3. Residual-based spectrum monitoring
  • 8.4. Monitoring of filterless optical networks
  • 8.5. Concluding remarks and future work
  • References
  • Chapter Nine: Machine learning and data science for low-margin optical networks
  • Abstract
  • 9.1. The shape of networks to come
  • 9.2. Current QoT margin taxonomy and design
  • 9.3. Generalization of optical network margins
  • 9.4. Large scale assessment of margins and their time variations in a deployed network
  • 9.5. Trade-off between capacity and availability
  • 9.6. Data-driven rate adaptation for automated network upgrades
  • 9.7. Machine learning for low-margin optical networks
  • 9.8. Conclusion
  • References
  • Chapter Ten: Machine learning for network security management, attacks, and intrusions detection
  • Abstract
  • Acknowledgements
  • 10.1. Physical layer security management
  • 10.2. Machine learning techniques for security diagnostics
  • 10.3. Accuracy of ML models in threat detection
  • 10.4. Runtime complexity of ML models
  • 10.5. Interpretability of ML models
  • 10.6. Open challenges
  • 10.7. Conclusion
  • References
  • Chapter Eleven: Machine learning for design and optimization of photonic devices
  • Abstract
  • 11.1. Introduction
  • 11.2. Deep neural network (DNN) models
  • 11.3. Nanophotonic power splitter
  • 11.4. Metasurfaces and plasmonics
  • 11.5. Other types of optical devices
  • 11.6. Discussion
  • 11.7. Conclusion
  • References
  • Index

Product details

  • No. of pages: 402
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 10, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323852272
  • eBook ISBN: 9780323852289

About the Editors

Alan Lau

Alan Pak Tao Lau received his B.A. Sc in Engineering Science (Electrical Option) and M.A.Sc. in Electrical and Computer Engineering from University of Toronto in 2003 and 2004, respectively. He obtained his Ph.D. in Electrical Engineering at Stanford University in 2008 and has joined the Hong Kong Polytechnic University where he is now a Professor. He was also a Visiting Professor at Stanford University in 2019. His research covers system characterization, performance monitoring, digital signal processing and machine learning applications of various optical communication systems and networks. He collaborates with industry and serves as technical program committee chairs of major conferences in Optical Communications and as Associate editor of Journal of Lightwave Technology since 2018.

Affiliations and Expertise

Professor, Hong Kong Polytechnic Univeristy, Hung Hom, Kowloon, Hong Kong

Faisal Khan

Faisal Nadeem Khan received the B.Sc. degree in electrical engineering (with Honours) from the University of Engineering and Technology Taxila, Pakistan, the M.Sc. degree in communications technology from the University of Ulm, Germany, and the Ph.D. degree in electronic and information engineering from The Hong Kong Polytechnic University, Hong Kong. He worked as a Senior Lecturer at the School of Electrical and Electronic Engineering of the University of Science Malaysia. He had also been a Research Fellow at the Photonics Research Centre, The Hong Kong Polytechnic University. Currently, he is an Associate Professor at Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University. His research interests include machine learning and digital signal processing techniques for optical communication systems and networks. He has authored or coauthored around 60 research articles in leading journals (including several invited papers) and key conferences as well as written two book chapters. He has been an invited speaker at various prestigious international conferences including Optical Fiber Communication (OFC), European Conference on Optical Communication (ECOC) and Signal Processing in Photonic Communications (SPPCom), among others.

Affiliations and Expertise

Associate Professor, Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, Beijing, China

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