As the next-generation wireless communication system, Sixth-Generation (6G) technologies are emerging, enabling various mobile edge networks that can revolutionize wireless communication and connectivity. By integrating Generative Artificial Intelligence (GAI) with mobile edge networks, generative mobile edge networks possess immense potential to enhance the intelligence and efficiency of wireless communication networks. In this article, we propose the concept of generative mobile edge networks and overview widely adopted GAI technologies and their applications in mobile edge networks. We then discuss the potential challenges faced by generative mobile edge networks in resource-constrained scenarios. To address these challenges, we develop a universal resource-efficient generative incentive mechanism framework, in which we design resource-efficient methods for network overhead reduction, formulate appropriate incentive mechanisms for the resource allocation problem, and utilize Generative Diffusion Models (GDMs) to find the optimal incentive mechanism solutions. Furthermore, we conduct a case study on resource-constrained mobile edge networks, employing model partition for efficient AI task offloading and proposing a GDM-based Stackelberg model to motivate edge devices to contribute computing resources for mobile edge intelligence. Finally, we propose several open directions that could contribute to the future popularity of generative mobile edge networks.

翻译：作为下一代无线通信系统，第六代（6G）技术正蓬勃发展，催生了多种能够革新无线通信与连接的移动边缘网络。通过将生成式人工智能（GAI）与移动边缘网络相融合，生成式移动边缘网络在提升无线通信网络的智能化程度与运行效率方面展现出巨大潜力。本文提出生成式移动边缘网络的概念，并综述了当前广泛采用的GAI技术及其在移动边缘网络中的应用。随后，我们探讨了资源受限场景下生成式移动边缘网络所面临的潜在挑战。为应对这些挑战，我们设计了一种通用的资源高效生成式激励机制框架：在该框架中，我们开发了降低网络开销的资源高效方法，针对资源分配问题构建了恰当的激励机制，并利用生成扩散模型（GDMs）寻找最优的激励机制解决方案。此外，我们以资源受限的移动边缘网络为案例，采用模型分区实现高效的AI任务卸载，并提出了基于GDM的Stackelberg模型，以激励边缘设备为移动边缘智能贡献计算资源。最后，我们提出了若干有望推动生成式移动边缘网络未来普及的开放研究方向。