A Phase-Optimized Physical Layer Security Scheme Based on Deep Reinforcement Learning

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Physical layer security (PLS) has been widely employed in the Internet-of-Things (IoT). Since most IoT devices are energy-constrained, the power efficiency of PLS schemes attracts increasing attention. Distinguished with existing power optimization-based PLS schemes, we propose a novel PLS scheme by optimizing the transmit phase to guarantee the security in an energy efficient way. On one hand, we add an initial phase shift at the legitimate transmitter to enlarge the difference between the received signal at the legitimate receiver and the eavesdropper. On the other hand, we employ a reconfigurable intelligent surface (RIS) to enhance the interference at the eavesdropper. Aiming at increasing the bit error rate (BER) at the eavesdropper with low energy cost, the phase optimization problem is solved by using a deep deterministic policy gradient (DDPG) algorithm, which is based on a deep reinforcement learning framework. Simulation results demonstrate that our proposed phase optimization scheme can effectively improve the security performance and reduce the power consumption at the transmitter.