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.
1. Part 1: Introduction - p. P1 2. Chapter 1: Wireless communications in the era of artificial intelligence - p. 3 3. Chapter 2: Overview of generative AI for next-generation wireless communications - p. 25 4. Part 2: Foundations of generative learning models - p. P2 5. Chapter 3: Fundamentals of generative adversarial networks - p. 39 6. Chapter 4: Fundamentals of variational autoencoder - p. 57 7. Chapter 5: Introduction of advanced generative AI models: diffusion and transformers - p. 77 8. Part 3: Generative AI for physical networking and communication theory - p. P3 9. Chapter 6: Generative AI for channel modeling - p. 97 10. Chapter 7: Generative AI for channel estimation - p. 127 11. Chapter 8: Generative AI for channel knowledge map construction - p. 147 12. Part 4: Generative AI for data transmission and communication architecture - p. P4 13. Chapter 9: Generative AI for joint source and channel coding - p. 173 14. Chapter 10: Generative AI for data-oriented communications - p. 201 15. Chapter 11: Generative AI for future communication: semantic communication - p. 241 16. Part 5: Generative AI for distributed networking and edge computing - p. P5 17. Chapter 12: Generative AI with federated learning - p. 261 18. Chapter 13: Generative AI for mobile edge computing - p. 289 19. Part 6: Generative AI for emerging technologies and applications - p. P6 20. Chapter 14: Generative AI and semantic communications - p. 309 21. Chapter 15: AI-generated content service in wireless communications - p. 325 22. Chapter 16: Trustworthy generative AI for wireless communications - p. 339 23. Chapter 17: Data management for generative AI in wireless communications - p. 367 24. Index - p. 379
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.
1. Part 1: Introduction - p. P1 2. Chapter 1: Wireless communications in the era of artificial intelligence - p. 3 3. Chapter 2: Overview of generative AI for next-generation wireless communications - p. 25 4. Part 2: Foundations of generative learning models - p. P2 5. Chapter 3: Fundamentals of generative adversarial networks - p. 39 6. Chapter 4: Fundamentals of variational autoencoder - p. 57 7. Chapter 5: Introduction of advanced generative AI models: diffusion and transformers - p. 77 8. Part 3: Generative AI for physical networking and communication theory - p. P3 9. Chapter 6: Generative AI for channel modeling - p. 97 10. Chapter 7: Generative AI for channel estimation - p. 127 11. Chapter 8: Generative AI for channel knowledge map construction - p. 147 12. Part 4: Generative AI for data transmission and communication architecture - p. P4 13. Chapter 9: Generative AI for joint source and channel coding - p. 173 14. Chapter 10: Generative AI for data-oriented communications - p. 201 15. Chapter 11: Generative AI for future communication: semantic communication - p. 241 16. Part 5: Generative AI for distributed networking and edge computing - p. P5 17. Chapter 12: Generative AI with federated learning - p. 261 18. Chapter 13: Generative AI for mobile edge computing - p. 289 19. Part 6: Generative AI for emerging technologies and applications - p. P6 20. Chapter 14: Generative AI and semantic communications - p. 309 21. Chapter 15: AI-generated content service in wireless communications - p. 325 22. Chapter 16: Trustworthy generative AI for wireless communications - p. 339 23. Chapter 17: Data management for generative AI in wireless communications - p. 367 24. Index - p. 379
