Deep Learning in Wireless Communications

The book offers a focused examination of deep learning-based wireless communication systems and their applications. While both principles and engineering practice are explored, greater emphasis is placed on the latter. The book offers an in-depth exploration of major topics such as cognitive spectrum intelligence, learning resource allocation optimization, transmission intelligence, learning traffic and mobility prediction, and security in wireless communication. Notably, the book provides a comprehensive and systematic treatment of practical issues related to intelligent wireless communication, making it particularly useful for those seeking to learn about practical solutions in AI-based wireless resource management. This book is a valuable resource for researchers, engineers, and graduate students in the fields of wireless communication, telecommunications, and related areas.

Table of Contents:
Introduction to Intelligence Wireless Communication.- Cognitive Spectrum Intelligence.- Learning Resource Allocation Optimization.- Transmission Intelligence.- Learning Traffic and Mobility Prediction.- Software Defined Networking.- Security in Wireless Communication.- 6G Driving Applications with Deep Learning.

About the Author :
Haijun Zhang (Fellow, IEEE) is currently a Full Professor and Dean at University of Science and Technology Beijing, China. He was a Postdoctoral Research Fellow in Department of Electrical and Computer Engineering, the University of British Columbia (UBC), Canada. He serves/served as Track Co-Chair of VTC Fall 2022 and WCNC 2020/2021, Symposium Chair of Globecom’19, TPC Co-Chair of INFOCOM 2018 Workshop on Integrating Edge Computing, Caching, and Offloading in Next Generation Networks, and General Co-Chair of GameNets’16. He serves as an Editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. He received the IEEE CSIM Technical Committee Best Journal Paper Award in 2018, IEEE ComSoc Young Author Best Paper Award in 2017, IEEE ComSoc Asia-Pacific Best Young Researcher Award in 2019. He is a Distinguished Lecturer of IEEE and IEEE Fellow. Ning Yang is an assistant researcher at the Institute of Automation, Chinese Academy of Sciences (CASIA). Her research areas include reinforcement learning and the application of reinforcement learning in combinatorial optimization. She received her Ph.D. from at University of Science and Technology Beijing in 2020, supervised by Prof. Haijun Zhang. Before joining CASIA, she was a visiting student working with Prof. Randall Berry from 2019 to 2020 at Electrical and Computer Engineering, Northwestern University. She received the Best Paper IEEE 87th Vehicular Technology Conference in 2018.