Abstract
This paper 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.
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