Generative Learning for Wireless Communications

Fundamentals and Applications
Erscheinungsjahr 2026
ISBN: 978-0-443-41497-8
Verlag: Elsevier Science

Buch, Englisch, 325 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Generative learning (GL) has emerged as an essential tool for data processing and network optimization in the broad area of next-generation communication systems. Generative Learning for Wireless Communications: Fundamentals and Applications provides a comprehensive and systematic tutorial for applying generative learning models to wireless communications. It explains the core concepts of state-of-the-art generative learning models, including generative adversarial nets, variational autoencoder, and other advanced models, such as transformers and diffusion models, and then shows their application to specific areas in wireless communications. Areas include physical networking, data transmission, edge computation, distributed learning, semantic communications, and other emerging fields in the next-generation wireless communications. To provide guidance on how to use GL techniques, each chapter includes a case study and an algorithm design for a realistic application. The book concludes with a discussion of the critical challenges of today and promising future directions of GL in wireless communications.

Autoren/Hrsg.

Zhang, Songyang

Zhang, Shuai

Huang, Chuan

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Drahtlostechnologie
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Mobilfunk

Inhaltsverzeichnis

Part I - Introduction
1. Wireless Communications in the Era of Artificial Intelligence
2. Overview of Generative AI models and Potentials in Wireless Communications

Part II - Foundations of Generative Learning Models
3. Fundamentals of Generative Adversarial Nets
4. Fundamentals of Variational Auto Encoder
5. Introduction of Advanced Generative AI Models: Diffusion and Transformers

Part III - Generative AI for Physical Networking and Communication Theory
6. Generative AI for Channel Modeling and Estimation
7. Generative AI for Integrated Sensing and Communications
8. Generative AI for Spectrum Sensing and Coverage Estimation

Part IV - Generative AI for Data Transmission and Communication Architecture
9. Generative AI for Joint Source and Channel Coding
10. Generative AI for Data-Oriented Communications
11. Generative AI for Semantic and Task-Oriented Communications

Part V - Generative AI for Distributed Networking and Edge Computing
12. Generative AI Empowered Federated Learning
113. Generative AI for Mobile Edge Computing

Part VI - Generative AI for Emerging Technologies and Applications
14. Generative AI and Digital Twin
15. AI-Generated Content Service
16. Trustworthy Generative AI for Wireless Communications
17. Data Management for Generative AI in Wireless Communications

Part VII - Conclusion
18. Summary, Insights and Future Directions

Über Autor(innen)

Zhang, Songyang
Dr. Songyang Zhang received the Ph.D. degree in Department of Electrical and Computer Engineering from the University of California at Davis, Davis, CA, USA, in 2021, where he was a Postdoctoral Research Associate from August 2021 to July 2023. He is currently an Assistant Professor with the Department of Electrical and Computer Engineering in University of Louisiana at Lafayette, Lafayette, LA, USA. His current research interests include machine learning, signal processing, IoT intelligence and wireless communications. He received the Best Paper Finalist in the 2020 IEEE International Conference on Image Processing, and was recognized as the Best TCSVT Reviewer of 2022 by IEEE Circuits and System Society.

Zhang, Shuai
Dr. Shuai Zhang is currently an Assistant Professor with the Ying Wu College of Computing at New Jersey Institute of Technology (NJIT). He received his Ph.D. from the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI) in 2021, supervised by Prof. Meng Wang. He received the Bachelor's degree in Electrical Engineering at the University of Science and Technology of China (USTC) in 2016. His research interests span deep learning, optimization, data science, and signal processing, with a particular emphasis on learning theory - the design of machine learning algorithms - as well as the development of efficient and trustworthy AI. Dr. Zhang has published in top AI conferences and IEEE Transactions, including NeurIPS, ICML, ICLR, TSP, TNNLS, and JSTSP

Huang, Chuan
Prof. Huang received the Ph.D. degree in Department of Electrical and Computer Engineering from Texas A&M University, College Station, TX, USA, in 2012. From 2012 to 2014, he had been postdoc researcher and research assistant professor in Arizona State University and Princeton University. Now, he is an associate professor in The Chinese University of Hong Kong, Shenzhen, China. His current research interests include artificial intelligence and wireless communications. He received the best paper awards in 2020 and 2023 IEEE GLOBECOM.