Learning to Navigate for Secure UAV Communication

In this paper, we investigate the navigation for unmanned aerial vehicle (UAV)s in the secure communication system, where we design the UAV's navigation/trajectory to ensure the Quality of Service (QoS) with the Base Station (BS) in the existence of multiple unknown-location dynamical eavesdroppers and jammers. To this end, we formulate a UAV trajectory optimization problem to minimize its mission completion time with QoS and security constraints. The imperfect information, dynamic communication environment, and non-convexity make the problem intractable. For these reasons, we propose a novel solution approach, namely Model-Assisted Reinforcement Learning (MARL) algorithm, where the communication system model is embedded into Deep Reinforcement Learning (DRL) framework to ensure secure communication and shorten the learning process. Numerical results show that our proposed methodology can safeguard security and find the shortest way to finish the mission.