Machine Learning for Future Fiber-Optic Communication Systems

1st Edition - February 10, 2022
Editors: Alan Pak Tao Lau, Faisal Nadeem Khan
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 2 2 7 - 2
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 2 2 8 - 9

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 wit… Read more

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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.

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

Alan Pak Tao 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 Nadeem 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