Energy Efficiency Maximization for RIS-Assisted UAV Covert Communication Based on DRL

This paper investigates a covert communication system enhanced by a reconfigurable intelligent surface (RIS), where an unmanned aerial vehicle (UAV) serves as the transmitter while a Warden moves freely within a certain range. To enhance both communication covertness and energy efficiency, we jointly optimize the UAV trajectory and RIS phase shifts to maximize system energy efficiency. The optimization problem is formulated as a Markov decision process (MDP), and a reinforcement learning-based approach, LSTM-DDQN, is proposed to exploit deep reinforcement learning (DRL) in dynamic environments. By integrating long short-term memory (LSTM) to capture temporal dependencies and double deep Q-network (DDQN) to mitigate Q-value overestimation, the proposed algorithm enhances stability and decision-making effectiveness. Simulation results demonstrate that LSTM-DDQN significantly improves energy efficiency while effectively reducing the risk of detection compared to conventional DDQN, validating its superiority in RIS-assisted UAV covert communication.