Generative Learning for Wireless Communications

The 1st Edition of Generative Learning for Wireless Communications presents a systematic tutorial on applying generative learning models to wireless communications. Authored by Chuan Huang, Shuai Zhang, and Songyang Zhang, this book establishes structured methods for integrating artificial intelligence techniques into communication system designs.

The coverage begins with fundamental concepts of generative learning models, explaining how generative adversarial networks and variational autoencoders process network signals and data. The text then transitions to advanced generative models, examining the operational mechanisms of transformers alongside diffusion models.

To translate theoretical concepts into functional tools, the volume includes algorithm designs, case studies, and practical guidance focused on generative AI deployment. It finishes with an analysis of critical challenges and future research directions in wireless network optimization.