Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

Unmanned air vehicles (UAVs) used as aerial base stations (ABSs) can provide communication services in areas where cellular network is not functional due to a calamity. ABSs provide high coverage and high data rates to the user because of the advantage of a high altitude. ABSs can be static or mobile; they can adjust their position according to real-time location of ground user and maintain a good line-of-sight link with ground users. In this paper, a reinforcement learning framework is proposed to maximize the number of served users by optimizing the ABS 3D location and power. We also design a reward function that prioritize the emergency users to establish a connection with the ABS using Q-learning. Simulation results reveal that the proposed scheme clearly outperforms the baseline schemes.