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.

将生成式人工智能（GAI）与边缘计算网络结合，提出了生成式移动边缘网络的概念，并概述了广泛采用的 GAI 技术及其在移动边缘网络中的应用。在资源受限场景下，通过设计资源高效的方法、制定适当的激励机制以及利用生成扩散模型（GDMs）找到最优的激励机制解决方案，解决了生成式移动边缘网络面临的挑战。通过案例研究提出了一种模型分区的方法以实现智能任务卸载，并提出了基于 GDM 的 Stackelberg 模型来激励边缘设备为移动边缘智能提供计算资源。最后，提出了几个能够促进生成式移动边缘网络未来发展的方向。