Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study
This work addresses the problem of inefficient wireless network management for network operators and service providers, but it is incremental as it applies existing DGM methods to a new domain.
The paper explores the application of deep generative models (DGMs) to improve the efficiency of wireless network management, proposing a DGM-empowered framework and demonstrating through a case study on network economics that diffusion models can generate effective contracts for incentivizing mobile AI-Generated Content services.
With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. 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 plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered framework for wireless network management is proposed, in which we elaborate 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, i.e., 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 the further research.