The continuous evolution of future mobile communication systems is heading towards the integration of communication and computing, with Mobile Edge Computing (MEC) emerging as a crucial means of implementing Artificial Intelligence (AI) computation. MEC could enhance the computational performance of wireless edge networks by offloading computing-intensive tasks to MEC servers. However, in edge computing scenarios, the sparse sample problem may lead to high costs of time-consuming model training. This paper proposes an MEC offloading decision and resource allocation solution that combines generative AI and deep reinforcement learning (DRL) for the communication-computing integration scenario in the 802.11ax Wi-Fi network. Initially, the optimal offloading policy is determined by the joint use of the Generative Diffusion Model (GDM) and the Twin Delayed DDPG (TD3) algorithm. Subsequently, resource allocation is accomplished by using the Hungarian algorithm. Simulation results demonstrate that the introduction of Generative AI significantly reduces model training costs, and the proposed solution exhibits significant reductions in system task processing latency and total energy consumption costs.

翻译：未来移动通信系统的持续演进正朝着通信与计算融合的方向发展，其中移动边缘计算（MEC）已成为实现人工智能（AI）计算的关键手段。通过将计算密集型任务卸载至MEC服务器，MEC可提升无线边缘网络的计算性能。然而，在边缘计算场景中，稀疏样本问题可能导致耗时的模型训练产生高昂成本。本文针对802.11ax Wi-Fi网络的通算融合场景，提出一种结合生成式AI与深度强化学习（DRL）的MEC卸载决策与资源分配方案。首先，通过联合使用生成扩散模型（GDM）与双延迟深度确定性策略梯度（TD3）算法确定最优卸载策略；随后，利用匈牙利算法完成资源分配。仿真结果表明，生成式AI的引入显著降低了模型训练成本，且所提方案在系统任务处理时延与总能耗成本方面均表现出显著降低。