Deep reinforcement learning enabled covert transmission with UAV
IEEE Wireless Communications Letters
School of Science / Security Research Institute
National Natural Science Foundation of China under Grant 62001116 and Grant 62271150 / Natural Science Foundation of Fujian Province under Grants 2020J05106, 2022J01081 / The University Synergy Innovation Program of Anhui Province under Grant GXXT-2022-055
This letter considers covert communications in the context of unmanned aerial vehicle (UAV) networks, where a UAV is employed as a base station to transmit covert data to a legitimate ground user, while ensuring that the data transmission cannot be detected by a warden. Aiming at maximizing the legitimate user’s average effective covert throughput (AECT), the UAV’s trajectory and transmit power are jointly optimized. Taking advantage of deep reinforcement learning (DRL) on solving dynamic and unpredictable problems, we develop a twin-delayed deep deterministic policy gradient aided covert transmission algorithm (TD3-CT), to determine the UAV’s optimal trajectory and transmit power. Furthermore, by introducing a reward shaping mechanism, the convergence of the algorithm is guaranteed. The experiment results show that the developed TD3-CT algorithm not only enables the covert transmission but also significantly improves its performance in termed of achieving a higher AECT, compared with the benchmark schemes.