Deep Reinforcement Learning-Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

Abstract

In this letter, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.