Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

TL;DR

This paper reviews deep generative models (DGMs) and demonstrates their application in enhancing wireless network management efficiency through a framework and case study involving diffusion models for network economics.

Contribution

It introduces a DGM-based framework for wireless network management and provides a case study applying diffusion models to generate contracts for incentivizing AI-generated content.

Findings

DGMs can effectively model complex patterns in wireless networks.

Diffusion models improve contract generation for network economics.

The framework addresses limitations of traditional network management approaches.

Abstract

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.