In this paper, we investigate an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) network under physical-layer security, where users can partially offload confidential and compute-intensive tasks to a computing access point (CAP) with the help of the IRS. We consider an eavesdropping environment, where an eavesdropper steals information from the communication. For the considered MEC network, we firstly design a secure data transmission rate to ensure physical-layer security. Moreover, we formulate the optimization target as minimizing the system cost linearized by the latency and energy consumption (ENCP). In further, we employ a deep deterministic policy gradient (DDPG) to optimize the system performance by allocating the offloading ratio and wireless bandwidth and computational capability to users. Finally, considering the impacts from different resources, based on DDPG, seeing our optimization strategy as one criterion, we designed other criteria with different resource allocation schemes. And some simulation results are given to demonstrate that our proposed criterion outperforms other criteria.

在本文中，我们研究了物理层安全下的智能反射面 (IRS) 辅助移动边缘计算 (MEC) 网络，其中用户可以在帮助下将机密和计算密集型任务部分卸载到计算接入点 (CAP)国税局的。我们考虑一个窃听环境，窃听者从通信中窃取信息。对于所考虑的 MEC 网络，我们首先设计了一个安全的数据传输速率，以确保物理层的安全。此外，我们将优化目标制定为最小化由延迟和能耗（ENCP）线性化的系统成本。此外，我们采用深度确定性策略梯度（DDPG）通过为用户分配卸载率、无线带宽和计算能力来优化系统性能。最后，考虑到不同资源的影响，基于DDPG，将我们的优化策略作为一个标准，我们设计了具有不同资源分配方案的其他标准。并给出了一些模拟结果来证明我们提出的标准优于其他标准。