Joint Trajectory and Power Optimization for Energy Efficient UAV Communication Using Deep Reinforcement Learning

Abstract

In recent years, unmanned aerial vehicles (UAVs) have been widely used in wireless communication, attracting intensive attentions. UAVs can not only serve as relays, but also serve as aerial base station for ground users (GUs). However, limited energy means that they cannot work for long and cover a limited area of services. In this paper, we investigate 2D UAV trajectory design and power allocation in order to maximize the UAV's service time and downlink throughput. Based on deep reinforcement learning, we propose a deep deterministic policy gradient (DDPG) algorithm for trajectory design and power allocation (TDPA) to solve the energy efficient and communication service quality problem. The simulation results show that TDPA can extend the service time of UAV, improve the communication service quality, and realize the maximization of downlink throughput, which are significantly improved compared with existing methods.