Kai Niu received the B.S. degree in information engineering and the Ph.D. degree in signal and information processing from Beijing University of Posts and Telecommunications， Beijing， China， in 1998 and 2003， respectively. He is currently a Professor of the School of Artificial Intelligence， Beijing University of Posts and Communications. His research interests include information theory， coding theory， and semantic communication. He has published more than 200 academic papers， and proposed high performance construction algorithm of polar codes applied in 5G standard， and won the first prize of Natural Science of Science and Technology Award of the Chinese Institute of Electronics. Ping Zhang is currently a Professor of the School of Information and Communication Engineering at Beijing University of Posts and Telecommunications， the Director of State Key Laboratory of Networking and Switching Technology， a member of IMT-2020 (5G) Experts Panel， a member of Experts Panel for China’s 6G development. He served as Chief Scientist of National Basic Research Program (973 Program)， an expert in Information Technology Division of National High-tech Research and Development program (863 Program)， and a member of Consultant Committee on International Cooperation of National Natural Science Foundation of China. His research interests mainly focus on wireless communication. He is an Academician of the Chinese Academy of Engineering (CAE).
目錄：
Chapter 1 Introduction 001
Chapter 2 Semantic Communication System and Synonymous Mapping 013
2.1 Notation Conventions 013
2.2 Semantic Communication System 014
2.3 Synonymous Mapping 016
Chapter 3 Semantic Entropy 019
3.1 Semantic Information Measures 019
3.2 Semantic Joint Entropy and Semantic Conditional Entropy 022
Chapter 4 Semantic Relative Entropy and Mutual Information 031
4.1 Semantic Relative Entropy 031
4.2 Semantic Mutual Information 036
Chapter 5 Semantic Channel Capacity and Semantic Rate-distortion 041
5.1 Semantic Channel Capacity 041
5.2 Semantic Rate-Distortion 042
Chapter 6 Semantic Lossless Source Coding 045
6.1 Asymptotic Equipartition Property and Synonymous Typical Set 045
6.2 Semantic Source Coding Theorem 051
6.3 Semantic Source Coding Method 054
Chapter 7 Semantic Channel Coding 057
7.1 Jointly Asymptotic Equipartition Property and Jointly Synonymous
Typical Set 057
7.2 Semantic Channel Coding Theorem 065
7.3 Semantic Channel Coding Method 073
Chapter 8 Semantic Lossy Source Coding 081
8.1 Semantic Distortion and Jointly Typical Set 081
8.2 Semantic Rate-Distortion Coding Theorem 085
Chapter 9 Semantic Information Measure of Continuous Message 091
9.1 Semantic Entropy and Semantic Mutual Information for Continuous
Message 091
9.2 Semantic Channel Capacity of Gaussian Channel 098
9.3 Semantic Channel Capacity of Band-limited Gaussian Channel 101
9.4 Semantic Rate-Distortion of Gaussian Source 104
Chapter 10 Semantic Joint Source Channel Coding 107
Chapter 11 Conclusions 111
Appendix 115
References 117
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