Deep-Reinforcement-Learning-Based IRS for Cooperative Jamming Networks Under Edge Computing
Tengyue Zhang, Hong Wen, Yixin Jiang, Jie Tang
Abstract
Effective energy design and secure communications are critical in the Internet of Things (IoT). A cooperative jamming (CJ) scheme based on deep reinforcement learning (DRL) is proposed for intelligent reflecting surface (IRS) aided secure cooperative network with eavesdroppers. Our goal is to maximize the average secrecy rate, or energy efficiency with secrecy constraints by jointly optimizing the transmit, jamming beamforming matrices, and IRS phase-shift matrix. In order to figure out the challenging nonconvex optimization problem, the deep deterministic policy gradient (DDPG) optimization algorithm is proposed to solve the energy efficiency maximization problem. In order to speed up task processing and reduce service delay, the edge computing server is employed for DRL training to improve computing speed and promote secure communication efficiency. By taking advantages of the features of proximity to end devices and computing resources, the edge devices provide a low latency efficient training support to the legal users and real-time control to the IRS units. By this way, a high security and low energy consumption IoT system is built. Numerical results show that the novel scheme improves the secrecy rate and energy efficiency compared with the benchmark scheme.