Abstract
In this paper, a novel resilient unmanned aerial vehicle (UAV) framework that enables UAVs to efficiently adjust their trajectories and antenna ports to serve disconnected users due to unexpected accidents is designed. In the proposed framework, a set of UAVs equipped with fluid antennas provide service for ground users. At the beginning, each UAV optimizes its three dimensional (3D) location and selects an antenna port to maximize the sum data rate of all users. During the service period, several UAVs may not be able to continue to serve ground users due to unexpected accidents. The remaining UAVs must adjust their trajectories and antenna ports to provide communication services for the users originally served by UAVs with accidents. This problem is formulated as an optimization problem that aims to maximize the total data rates of all users during the entire service period including the period that all UAVs can provide service, the period that some UAVs cannot provide service and the remaining UAVs must adjust their trajectories and antenna ports, and the period that the remaining UAVs find fixed locations to serve users. To solve this problem, an attention and gate recurrent unit (GRU) based reinforcement learning (AGRL) method is designed. In this method, the GRUs are utilized to capture previous UAV actions including trajectories and antenna port selections and states. The transformer is used to analyze the importance of previous UAV actions and states, thus further improving the total data rates of all users. To further improve the training speed of the designed AGRL method, we mathematically derive the optimally initial UAV locations. Simulation results show that the proposed AGRL method can improve the expected data rate of all users by up to 9.89% and 10.19% compared to the value function decomposition RL (VDRL) method and the proposed AGRL method without optimizing antenna port selection.