Generative Learning for Wireless Communications: Fundamentals and Applications provides a comprehensive and systematic tutorial for applying generative learning models to wireless communications. The book 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.

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.

• 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