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.

翻译：随着扩散模型和ChatGPT的巨大成功，深度生成模型(DGMs)自2022年起经历了爆发式增长。得益于其表征复杂模式和生成合理样本的卓越能力，DGMs不仅局限于内容生成，还被广泛应用于物联网、元宇宙和数字孪生领域。本文探讨了DGMs在一项关键任务（即提升无线网络管理效率）中的应用。具体而言，我们首先概述了生成式人工智能及三种代表性DGMs。随后，提出了一种基于DGM的无线网络管理框架，详细阐述了传统网络管理方法存在的问题、DGMs为何能高效解决这些问题，以及应用DGMs管理无线网络的分步工作流程。进一步地，我们开展了网络经济学案例研究，利用最先进的DGM模型（即扩散模型）生成有效合约以激励移动人工智能生成内容(AIGC)服务。最后，我们讨论了值得进一步研究的重要开放方向。