Trajectory optimization for UAV-enabled relaying with reinforcement learning

In this paper, we investigate the application of the Unmanned Aerial Vehicle (UAV)-enabled relaying system in emergency communications, where one UAV is applied as a relay to help transmit information from ground users to a Base Station (BS). We maximize the total transmitted data from the users to the BS, by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the UAV. To solve this non-convex optimization problem, we propose the traditional Convex Optimization (CO) and the Reinforcement Learning (RL)-based approaches. Specifically, we apply the block coordinate descent and successive convex approximation techniques in the CO approach, while applying the soft actor-critic algorithm in the RL approach. The simulation results show that both approaches can solve the proposed optimization problem and obtain good results. Moreover, the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.