Joint Optimization of Trajectory Control, Resource Allocation and User Association Based on DRL for Multi-Fixed-Wing UAV Networks

Owing to the abundance of onboard energy and wide coverage, fixed-wing unmanned aerial vehicles (FW-UAVs) have better capabilities to serve as aerial base stations, thereby extending communication coverage and improving the performance of ground wireless communication networks. Therefore, the FW-UAV is regarded as one of the essential components of the sixth-generation (6G) communication networks. However, due to its inability to hover, a single FW-UAV may only serve a few mobile users (MUs) at a given time which introduces challenges in ensuring uninterrupted service. Additionally, the limited communication resource further impacts the quality of service (QoS). In order to improve the QoS and guarantee the uninterrupted services of the MUs that are located in a wide range, we consider a multi-FW-UAV communication network to maximize the cumulative throughput by optimizing the trajectory, power control, user association, and subcarrier allocation policy jointly. Since the above problem is non-convex, we first decompose the optimization problem into two subproblems i.e., the trajectory optimization subproblem and the power control, user association, and subcarrier allocation policy optimization subproblem. Then, a multi-agent deep reinforcement learning (MA-DRL)-based joint optimization scheme is proposed to optimize the two subproblems jointly. Simulation results demonstrate that the proposed scheme can maximize the cumulative throughput and gain superior performance compared to the benchmark schemes.