Overview

This textbook provides a comprehensive, step-by-step guide to Semantic Communications, focusing on how deep learning enhances its performance in modern communication systems. The purpose of the book is to offer students, researchers, and engineers a solid understanding of the theoretical principles and practical applications of semantic communication. The text builds from foundational concepts to advanced topics, making it suitable for graduate-level courses and beyond, while also serving as a reference for professionals looking to incorporate semantic communication into real-world systems. By moving beyond conventional data transmission to focus on meaning and intent, this textbook highlights how AI and deep learning are revolutionizing joint source-channel coding, bandwidth optimization, and task-oriented communications. With a strong emphasis on 6G and next-generation networks, this book offers a comprehensive guide to the theoretical foundations and practical applications of semantic communication, making it an essential resource for researchers, students, and professionals shaping the future of AI-driven telecommunications. The book features discussion questions, MATLAB or Python code for experiments and simulations related to semantic communication systems, PowerPoint slides for instructors, and a solutions manual. Combines theoretical principles with practical implementations of semantic communication systems; Addresses the foundational concepts and the hands-on application of deep learning to improve communication efficiency; Features discussion questions, MATLAB code for experiments, PowerPoint slides for instructors, and a solutions manual.

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9783032110046

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Author Information

Wei Chen is a Professor at Beijing Jiaotong University, China. He received the B.Eng. degree and M.Eng. degree from Beijing University of Posts and Telecommunications, China, in 2006 and 2009, respectively, and the Ph.D. degree in Computer Science from the University of Cambridge, UK, in 2013. Later, he was a Research Associate with the Computer Laboratory, University of Cambridge from 2013 to 2016. His current research interests include semantic communications, AL/ML for PHY and sparse signal processing. He was a recipient of the 2013 IET Wireless Sensor Systems Premium Award, the 2017 International Conference on Computer Vision (ICCV) Young Researcher Award, the 2019 CCF-Tencent Rhino Bird Innovation Award, the 2020 IWCMC 5G-EWNAT Workshop Best Paper Award, and 2023 IEEE/CIC ICCC Best Paper Award. He has served as the editor and symposium chair of several IEEE/IET journals and international conferences. Recently, he serves as the lead guest editor for IEEE JSTSP Special Issue on Intelligent Signal Processing and Learning for Next Generation Multiple Access. Zhijin Qin is an Associate Professor at Tsinghua University, China. She was with Queen Mary University of London and Lancaster University as a lecturer as well as with Imperial College London as a research associate from 2016 to 2022. She obtained her PhD degree in 2016 and the bachelor degree in 2012. Her research interests include semantic communications and sparse signal processing in wireless communications. She serves as an area editor of IEEE JSAC Series on Machine learning in Communications and Networks, an associate editor of IEEE Transactions on Communications, IEEE Transactions on Cognitive Communications and Networking, and IEEE Communications Letters. Dr Qin has served as the symposium co-chair for IEEE VTC Fall 2019 and IEEE Globecom 2020/2021. She received the 2017 IEEE Globecom Best Paper Award, the 2018 IEEE Signal Processing Society Young Author Best Paper Award, the 2021 IEEE Communications Society SPCC Early Achievement Award, the 2022 IEEE Communications Society Fred W. Ellersick Prize, the 2023 IEEE ICC Best Paper Award, and 2023 IEEE Signal Processing Society Best Paper Award.

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