This focused textbook demonstrates cutting-edge concepts at the intersection of machine learning (ML) and wireless communications, providing students with a deep and insightful understanding of this emerging field. It introduces students to a broad array of ML tools for effective wireless system design, and supports them in exploring ways in which future wireless networks can be designed to enable more effective deployment of federated and distributed learning techniques to enable AI systems. Requiring no previous knowledge of ML, this accessible introduction includes over 20 worked examples demonstrating the use of theoretical principles to address real-world challenges, and over 100 end-of-chapter exercises to cement student understanding, including hands-on computational exercises using Python. Accompanied by code supplements and solutions for instructors, this is the ideal textbook for a single-semester senior undergraduate or graduate course for students in electrical engineering, and an invaluable reference for academic researchers and professional engineers in wireless communications.
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This concise textbook demonstrates cutting-edge concepts at the emerging intersection of machine learning and wireless communications.
Preface; Notation;
1. Introduction;
2. Channel modeling, estimation, and
compression;
3. Learning receiver design: signal detection and channel
decoding;
4. End-to-end learning of wireless communication systems;
5.
Learning resource allocation in wireless networks;
6. Wireless for AI:
distributed and federated learning; References; Index.
Le Liang is a Professor in the School of Information Science and Engineering at Southeast University, Nanjing. He is a member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society and was the Founding Technical Program Co-chair of the IEEE International Conference on Machine Learning for Communication and Networking. Shi Jin is a Chair Professor at Southeast University, Nanjing. He is an IEEE Fellow and Area Editor for the IEEE Transactions on Communications. He received the Stephen O. Rice Prize Paper Award in 2011, the IEEE Jack Neubauer Memorial Award in 2023, and the IEEE Marconi Prize Paper Award in Wireless Communications in 2024. Hao Ye is an Assistant Professor in Electrical and Computer Engineering at UC Santa Cruz, and previously worked as a Machine Learning Researcher at Qualcomm AI Research. He was awarded the IEEE Communications Society Fred W. Ellersick Prize in 2022. Geoffrey Ye Li is a Chair Professor at Imperial College, London. He is a Fellow of the IEEE, IET and Royal Academy of Engineering, and received the IEEE Eric E. Sumner Award in 2024, the Fred W. Ellersick Prize Paper Award in 2022, and the IEEE Communications Society Edwin Howard Armstrong Achievement Award in 2019, among others.
