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

Liang Wang, Kezhi Wang*, Cunhua Pan, Nauman Aslam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
18 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)6543 - 6553
Number of pages11
JournalIEEE Transactions on Mobile Computing
Volume22
Issue number1
Early online date29 Aug 2022
DOIs
Publication statusPublished - 1 Nov 2023

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