Machine Learning for Future Fiber-Optic Communication Systems - Softcover

Softcover

ISBN 10: 0323852270 ISBN 13: 9780323852272

Publisher: Academic Press, 2022

Synopsis

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.

• 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

About this title

About the Authors

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.

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.

From the Back Cover

Machine learning (ML) is being hailed as a new direction of innovation to transform future optical communication systems. Signal processing paradigms based on ML are being considered to solve certain critical problems in optical networks that cannot be easily tackled using conventional approaches.

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 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 their problems, and provide intelligent and efficient services to the end users.

With an 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.

Edition details

• Publisher: Academic Press
• Publication date: 2022
• Language: English
• ISBN 10: 0323852270
• ISBN 13: 9780323852272
• Binding: Paperback
• Edition number: 1
• Number of pages: 402
• Editor: Lau Alan Pak Tao, Khan Faisal Nadeem