Machine Learning for Wireless Communications and Networking

Machine Learning for Wireless Communications and Networking

An Introduction

1st Edition - February 1, 2023

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  • Author: Kwang-Cheng Chen
  • Paperback ISBN: 9780323912389

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Machine Learning for Wireless Communications and Networking: An Introduction provides an easy-to-understand introduction to machine learning methods and techniques and their application to wireless communications. The book covers a wide range of machine learning techniques, starting with concepts related to statistical signal processing (i.e.,  decision/detection and estimation), taking advantage of the commonality of knowledge between statistical learning and statistical communication theory that the electronic engineer will be familiar with. Each chapter focuses on a class of machine learning techniques, clearly explaining the principles with a supporting range of examples in general wireless communications, wireless networks, sensor networks, and signal processing. Every chapter also has a dedicated section applying machine learning techniques to specific, state-of-the-art wireless network applications. This book will be ideal for graduate and senior undergraduate students in wireless communications and networking who need to understand and apply machine learning techniques, researchers in wireless communications, signal processing, wireless network professionals who need background knowledge in machine learning for wireless systems and networks, and engineers and professionals in the wireless communications and networking industry seeking to learn this important new technology which is having a major impact in the field.

Key Features

  • Provides easily accessible and understandable machine learning knowledge for the reader with a background in communications, networking and signal processing
  • Presents a comprehensive and easy-to-understand discussion of machine learning techniques that are supported with a range of examples
  • Includes a detailed presentation of statistical inference as a foundation for machine learning while also providing a foundation in general for communications engineering, signal processing, control and automation, cyber-physical systems, Internet of Things and cybersecurity
  • Presents statistical signal processing principles that are commonly used in communications engineering, signal processing, stochastic control and networking


Graduate students, academic researchers, R&D engineers wanting to learn and apply machine learning techniques to wireless communications and networking

Table of Contents

  • 1. Basic Concepts of Machine Learning

    2. Statistical Inference
    Introduction of statistical foundation for hypothesis testing (for decision/detection) and
    Applications: Internet tomography and cognitive radio network tomography

    3. Regression
    Linear regression, multiple regression, ridge regression, LASSO, and logistic regression
    Applications: rapid channel modeling

    4. Classification
    Pattern recognition, support vector machine, and unsupervised learning for
    Applications: signal detection and interception, spectrum sensing, radio access network

    5. Deep Learning and Big Data Driven Methodology
    Principles of artificial neural network, convolution NN, recurrent NN, deep learning, big
    data analytics
    Applications: mobility pattern prediction, vehicle fleet management, traffic prediction

    6. Federated Learning
    Applications: wireless sensor networks and massive machine-type communication

    7. Generative Adversarial Network
    Applications: wireless network synchronization

    8. Reinforcement Learning
    Markov decision processes, multi-armed bandit problem, and reinforcement learning
    Applications: radio resource management, cognitive radio, beamforming in MIMO

    9. Wireless Robotic Communications: Wireless Networked Multi-Agent Systems
    Applications: Interplay between wireless techniques and multi-agent systems

    10. Naïve Bayesian, Decision Tree, and Random Forest
    Applications: predictive mobility management for ultra-low latency wireless networking

    11. Bayesian Networks
    Graphical models and causality in AI
    Applications: Interference management

    12. Future Machine Learning Based Network Architecture
    Future mobile network architecture to incorporate machine learning

Product details

  • No. of pages: 375
  • Language: English
  • Copyright: © Academic Press 2023
  • Published: February 1, 2023
  • Imprint: Academic Press
  • Paperback ISBN: 9780323912389

About the Author

Kwang-Cheng Chen

Dr. Chen received the B.S. from the National Taiwan University in 1983, and the M.S. and Ph.D from the University of Maryland, College Park, in 1987 and 1989, all in electrical engineering. From 1987 to 1998, Dr. Chen worked with SSE, COMSAT, IBM Thomas J. Watson Research Center, and National Tsing Hua University, in mobile communications and networks. From 1998-2016, Dr. Chen was with National Taiwan University, Taipei, Taiwan, where he was Distinguished Professor and Irving T. Ho Chair Professor, and served as the Director, Graduate Institute of Communication Engineering, Director, Communication Research Center, and Associate Dean, College of Electrical Engineering and Computer Science. He has authored and co-authored near 300 IEEE papers, 23 granted US patents, 3 books, including a few Highly Cited Papers. Dr. Chen is an IEEE Fellow and a recipient for a number of prestigious awards including 2011 IEEE COMSOC WTC Wireless Communication Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. His technical leadership results in Best Paper Awards in major IEEE Conferences like ICC, Globecom, and PIMRC.

Affiliations and Expertise

University of South Florida, USA

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