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

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

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