Deep Q-Network Based Dynamic Trajectory Design for UAV-Aided Emergency Communications

In this paper, an unmanned aerial vehicle (UAV)-aided wireless emergence communication system is studied, where an UAV is deployed to support ground user equipments (UEs) for emergence communications. We aim to maximize the number of the UEs served, the fairness, and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of UEs. We propose a Deep Q-Network (DQN) based algorithm, which involves the well-known Deep Neural Network (DNN) and Q-Learning, to solve the UAV trajectory problem. Then, based on the optimized UAV trajectory, we further propose a successive convex approximation (SCA) based algorithm to tackle the power control problem for each UE. Numerical simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness, the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization