Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach

In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.

Automated summary

This paper studies the intelligent reflecting surface-aided unmanned aerial vehicle communication system, which improves the communication quality between UAV and user equipment by using multiple intelligent reflecting surfaces. The goal is to maximize the energy efficiency of the system by jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements. Two algorithms based on deep Q-network and deep deterministic policy gradient are proposed to handle the complex and dynamic environment. Experimental results show that the proposed algorithms outperform traditional solutions.