Semantic Wireless Communications

Joint Communication and Computation Perspective
Erscheinungsjahr 2026
ISBN: 978-3-032-36726-6
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 188 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Wireless Networks

This book covers the foundations and system design of semantic wireless communications from a joint communication and computation perspective. It introduces semantic and task-oriented communication, semantic information metrics, and key modeling tools such as autoencoders, semantic entropy, knowledge graphs, and probability graphs. It then develops resource-allocation methods for semantic similarity maximization and equivalent-rate and energy optimization under practical wireless constraints.

Later chapters connect these ideas to emerging 6G scenarios. This includes probabilistic semantic communication over space-air-ground integrated networks, federated learning for audio semantic communication, integrated sensing/computing/semantic communication for e-healthcare, semantic feature multiple access with generative AI, and security and privacy. 

This book is intended for researchers and graduate students, as well as engineers building wireless, edge-intelligence, and AI-native network systems. It combines mathematical models, optimization formulations, algorithm design, simulation studies, and open research directions.

Zielgruppe

Research

Autoren/Hrsg.

Yang, Zhaohui

Chen, Mingzhe

Yener, Aylin

Niyato, Dusit

Zhang, Zhaoyang

Cui, Shuguang

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Drahtlostechnologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware

Inhaltsverzeichnis

Chapter 1: Introduction.- 1.1 Semantic Communications for 5G and Beyond.- 1.2 Previous Work.- 1.3 Motivation and Outline.- Chapter 2: Physical Layer for Semantic Wireless Communications.- 2.1 Semantic Information Theory.- 2.2 Knowledge Graph Coding.- 2.3 Probability Graph Coding.- 2.4 Joint Source-Channel Coding.- 2.5 Challenge.- Chapter 3: Semantic Wireless Communications: Communication and Computation Models.- 3.1 Semantic Compression Model.- 3.2 Semantic Accuracy Model.- 3.3 Computation Model for Transmitter.- 3.4 Communication Model.- 3.5 Computation Model for Receiver.- 3.6 Summary and Insights.- Chapter 4: Resource Allocation for Semantic Wireless Communications.- 4.1 Key Performance Indicators.- 4.2 Single-User Semantic Wireless Communications Systems.- 4.3 Multiuser Semantic Wireless Communications Systems.- 4.4 Multi-tier Semantic Wireless Communications Systems.- Chapter 5: Semantic Wireless Communications with Emerging Technologies.- 5.1 Federated Learning.- 5.2 Joint Communication, Sensing, and Computation.- 5.3 Metaverse.- 5.4 Blockchain.- 5.5 Split Learning.- 5.6 UAV, Satiate, and Marine.- 5.7 Reconfigurable Intelligence Surface.- 5.8 Next Generation Multiple Access.- Chapter 6: High-level Challenges and Future Directions.- 6.1   Network Architectures and Protocols.- 6.2 Energy Efficient SWC.- 6.3 SWC-Aware Mobile Edge Computing.- 6.4 Security and Privacy Issues.

Über Autor(innen)

Zhaohui Yang (Member, IEEE) received the B.S. degree in information science and engineering from Chien-Shiung Wu Honors College, Southeast University, Nanjing, China, in June 2014, and the Ph.D. degree in communication and information system with the National Mobile Communications Research Laboratory, Southeast University, Nanjing, China, in May 2018. From 2018 to 2020, he was a Postdoctoral Research Associate, King’s College London, UK. From 2020 to 2022, he was a Research Fellow, University College London, UK. He is currently a ZJU young Professor with College of Information Science and Electronic Engineering, Zhejiang University.

Mingzhe Chen received his Ph.D. from the Beijing University of Posts and Telecommunications, Beijing, China, in 2019. From 2016 to 2019, he was a Visiting Researcher at the Department of Electrical and Computer Engineering, at Virginia Tech. From 2019 to 2021, he was a Postdoctoral Research Associate with the Department of Electrical and Computer Engineering at Princeton University. In 2022, he worked as an AI Researcher at Ericsson Research, USA. Currently, he is an Assistant Professor at the Department of Electrical and Computer Engineering and the Institute of Data Science and Computing at the University of Miami. His research interests include federated learning, reinforcement learning, virtual reality, unmanned aerial vehicles, and the Internet of Things.

Aylin Yener holds the Roy and Lois Chope Chair in Engineering at The Ohio State University since January 2020, and is Professor of Electrical and Computer Engineering, Professor of Computer Science and Engineering and Professor of Integrated Systems Engineering.  Until December 2019, she was a Distinguished Professor of Electrical Engineering and a Dean’s Fellow at Penn State, where she joined in 2002 as an assistant professor. In 2008-2009, she was a visiting associate professor in the Electrical Engineering Department at Stanford University, and in 2016-2017 she was a visiting professor in the same department. Yener is a fellow of the American Association for Advancement Science (AAAS), and a fellow of the Institute of Electrical and Electronics Engineers  (IEEE).

Dusit Niyato (M'09-SM'15-F'17) is a professor in the College of Computing and Data Science, at Nanyang Technological University, Singapore. He received B.Eng. from King Mongkuts Institute of Technology Ladkrabang (KMITL), Thailand and Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada. His research interests are in the areas of mobile generative AI, edge general intelligence, quantum computing and networking, and incentive mechanism design.

Zhaoyang Zhang received the Ph.D. degree from Zhejiang University, Hangzhou, China, in 1998. He is currently a Qiushi Distinguished Professor with Zhejiang University. He has co-authored more than 200 IEEE journal articles. His research interests are mainly focused on the fundamental aspects of wireless communications and networking, with an emphasis on AI-empowered communications and networking, integrated communication, sensing and computing, and field signal processing and communication theory. He was a co-recipient of the 2024 IEEE Leonard G. Abraham Prize and received about ten best paper awards or student travel grants at international conferences like IEEE ICC 2019, GLOBECOM 2020, ISIT 2023, and WCNC 2024.

Shuguang Cui received his Ph.D. in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. His current research interests focus on the merging between AI and communication networks. In 2023, he won the IEEE Marconi Best Paper Award, got elected as a Fellow of both Canadian Academy of Engineering and the Royal Society of Canada.