TY - JOUR
T1 - Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications
T2 - A Deep Reinforcement Learning Approach
AU - Wang, Liang
AU - Wang, Kezhi
AU - Pan, Cunhua
AU - Aslam, Nauman
PY - 2022/8/29
Y1 - 2022/8/29
N2 - 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.
AB - 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.
KW - Deep Reinforcement Learning,
KW - Intelligent Reflecting Surface
KW - UAV communications
KW - Wireless communication
KW - Array signal processing
KW - intelligent reflecting surface
KW - Rotors
KW - Autonomous aerial vehicles
KW - Deep reinforcement learning
KW - Minimization
KW - Energy efficiency
KW - Trajectory
UR - http://www.scopus.com/inward/record.url?scp=85137861255&partnerID=8YFLogxK
U2 - 10.1109/TMC.2022.3200998
DO - 10.1109/TMC.2022.3200998
M3 - Article
SP - 1
EP - 11
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
SN - 1536-1233
ER -