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I. INTRODUCTION  
A. State-of-the-Art  
B. Motivations and Contributions  
C. Paper Organization  

II. GENERATIVE AI: PRELIMINARY  
A. History of Generative AI  
B. Primitive Structure of GAI  
C. Multi-modal GAI  
D. Representative GAI Models  

III. APPLICATION OF GENERATIVE AI IN NETWORK MANAGEMENT  
A. Network Management  
B. Resource Allocation  
C. Network Routing  
D. Channel Estimation  
E. Lessons Learned and Key Takeaways  

IV. APPLICATION OF GAI IN WIRELESS SECURITY  
A. Sensitive Data Obscurity  
B. Intrusion Detection  
C. Jamming Attacks  
D. Generative Steganography  
E. Lessons Learned and Key Takeaways  

V. APPLICATION OF GAI IN SEMANTIC COMMUNICATION  
A. Knowledge Abstraction  
B. Data Compression  
C. Generative AI and Distributed Learning  
D. Lessons Learned and Key Takeaways  

VI. CHALLENGES AND FUTURE DIRECTIONS  
A. Technical Challenges  
B. Lack of Interpretability  
C. Ethical and Legal Concerns  

VII. CONCLUSION  
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