Generative Learning for Wireless Communications

Nyhet

Fundamentals and Applications

Häftad, Engelska, 2026

AvSongyang Zhang,Shuai Zhang,Chuan Huang

1 999 kr

Kommande

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.

Explains the fundamental concepts of the state-of-the-art generative learning models
Presents the most advanced methods of generative AI in wireless communications
Gives practical guidance on how to apply generative AI in wireless communications
Includes case studies and algorithm designs
Presents the critical challenges of GL today and promising future directions

Produktinformation

Utgivningsdatum2026-07-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor325
FörlagElsevier Science
ISBN9780443414978

Tillhör följande kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Dr. Songyang Zhang received the Ph.D. degree from the Department of Electrical and Computer Engineering at the University of California, Davis, CA, USA. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Louisiana at Lafayette, Lafayette, LA, USA.Dr. Shuai Zhang received his Ph.D. degree from the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI) in 2021. He is currently an Assistant Professor in the Ying Wu College of Computing at the New Jersey Institute of Technology (NJIT), NJ, USA.Prof. Chuan Huang received his Ph.D. degree from the Department of Electrical and Computer Engineering at Texas A&M University, College Station, TX, USA, in 2012. He is currently a Professor in Shenzhen Institute for Advanced Study at University of Electronic Science and Technology of China, Shenzhen, China.

Part I - Introduction1. Wireless Communications in the Era of Artificial Intelligence2. Overview of Generative AI models and Potentials in Wireless CommunicationsPart II – Foundations of Generative Learning Models3. Fundamentals of Generative Adversarial Nets4. Fundamentals of Variational Auto Encoder5. Introduction of Advanced Generative AI Models: Diffusion and TransformersPart III – Generative AI for Physical Networking and Communication Theory6. Generative AI for Channel Modeling and Estimation7. Generative AI for Integrated Sensing and Communications8. Generative AI for Spectrum Sensing and Coverage EstimationPart IV – Generative AI for Data Transmission and Communication Architecture9. Generative AI for Joint Source and Channel Coding10. Generative AI for Data-Oriented Communications11. Generative AI for Semantic and Task-Oriented CommunicationsPart V – Generative AI for Distributed Networking and Edge Computing12. Generative AI Empowered Federated Learning113. Generative AI for Mobile Edge ComputingPart VI – Generative AI for Emerging Technologies and Applications14. Generative AI and Digital Twin15. AI-Generated Content Service16. Trustworthy Generative AI for Wireless Communications17. Data Management for Generative AI in Wireless CommunicationsPart VII – Conclusion18. Summary, Insights and Future Directions