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Wireless Communications and Machine Learning

Wireless Communications and Machine Learning

Wireless Communications and Machine Learning

Le Liang, Southeast University, Nanjing
Shi Jin, Southeast University, Nanjing
Hao Ye, University of California, Santa Cruz
Geoffrey Ye Li, Imperial College of Science, Technology and Medicine, London
July 2025
Not yet published - available from June 2025
Hardback
9781009232203
£69.99
GBP
Hardback
USD
Adobe eBook Reader

    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.

    • 'key processing functionalities' should be 'key communication receiver functionalities'
    • Coverage includes deep learning for improved channel modeling and estimation; learning-based methods for key processing functionalities; end-to-end learning-enabled system design, and optimized radio resource allocation
    • Features a discussion of “wireless for AI” that extends the “AI for wireless” toolset to give more efficient machine learning training and inference
    • Requires no previous knowledge of machine learning, instead building on existing student understanding of probability, signals and systems, and wireless communications

    Product details

    July 2025
    Hardback
    9781009232203
    306 pages
    254 × 178 mm
    Not yet published - available from June 2025

    Table of Contents

    • 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.
      Authors
    • Le Liang , Southeast University, Nanjing

      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 , Southeast University, Nanjing

      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 , University of California, Santa Cruz

      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 , Imperial College of Science, Technology and Medicine, London

      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.