Intelligent Covert ISAC via RIS: A Reinforcement Learning Approach

The combination of reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) can improve the resource utilization in non-line-of-sight scenarios. To sense the target with high accuracy, it is necessary to directionally reflect the sensing signal toward the sensing target via the RIS, improving the sensing performance. However, enhancing signal quality may increase the risk of information leakage when the sensing target is the warden. Against this background, we investigate a covert transmission problem in an RIS assisted ISAC system. Specifically, we obtain a tractable form of covertness constraint in terms of minimum detection error probability via the optimal detection threshold. Then, we maximize the sum covert transmission rate by jointly optimizing the beamforming of confidential signal and jamming signal as well as the RIS’s phase shift, while ensuring the reliability, covertness and sensing constraints. Owing to the effectiveness of the deep reinforcement learning algorithm in processing high-dimensional data and making intelligent decision, we propose a twins-deep deterministic policy gradient-based joint covert beamforming and the phase shift of RIS optimization (TD3-CBP) algorithm to solve the above non-convex problem. Finally, simulation results demonstrate the effectiveness of the proposed TD3-CBP algorithm in the covertness performance, achieving an average of 16.1% higher sum covert transmission rate than the benchmark algorithms.