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Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study
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DOI：10.1109/MWC.009.2300165.png)
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With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate realistic samples. In this article, we explore the applications of DGMs in a crucial task, that is, improving the efficiency of wireless network management. Specifically, we first overview the generative AI, as well as three representative DGMs. Then, we propose a DGM-empowered framework for wireless network management, in which we elaborate on the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, that is, diffusion model, to generate effective contracts for incentivizing the mobile AI-generated content (AIGC) services. Last but not least, we discuss important open directions for further research.
Keyword:
Wireless networks
Training
Computational modeling
Internet of Things
Generative AI
Task analysis
Generative adversarial networks
期刊
IF:
11.5
论文数:
2.8K
被引数:
1.3W
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