UAV Trajectory and Phase Shift Design for IRS-Assisted UAV Data Collection: A Deep Reinforcement Learning Approach

Abstract

Unmanned aerial vehicle (UAV) is becoming an effective solution for collecting IoT data. However, due to its limited battery capacity, UAV cannot complete data collection tasks over broad areas or a long time, which is incompatible with attaining fairness and high energy efficiency in data collection. To address the above challenges, the intelligent reflecting surface (IRS) is introduced as a solution. It can enhance communication by separately controlling the phase shift of each element. This paper investigates the problem of the IRS assisting a recharged UAV for data collection. We propose a proximal policy optimization (PPO)-based algorithm to jointly optimize the phase shift of IRS and the flight trajectory of UAV. To prevent crashes, we allow the UAV to return to the charging station when its battery is lower than the threshold. Simulation results show that the proposed method outperforms existing solutions in terms of fairness and energy efficiency.