Generative Learning for Wireless Communications: Fundamentals and Applications

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.

Table of Contents:
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

About the Author :
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.