Joint Optimization in Intelligent Reflecting Surface-Aided UAV Communication for Multiaccess Edge Computing

Abstract

Intelligent reflecting surface (IRS) is a key enabling technology for b5G and 6G networks, which can provide a reconfigurable electromagnetic environment while reducing energy consumption. In this article, the communication link between user equipment (UE) and the base station (BS) is severely blocked, so we deployed IRS on the Unmanned Aerial Vehicle (UAV) to assist UE for offloading the computing task to the multiaccess edge computing (MEC) server on the base station, which provides mobile users with low-latency edge computing services. By jointly optimizing active beamforming of UE transmitter, passive beamforming of the IRS, UAV hovering position, and computing task scheduling, the response time of user tasks is minimized. In order to solve this complex nonconvex problem, we propose an alternating optimization (AO) algorithm combined with the genetic algorithm to decouple the problem, alternate optimization, until the convergence condition is met, to find the approximate optimal solution of the problem. Numerical results show that with the assistance of IRS, MIMO channels can significantly improve the performance of edge computing and meet the needs of users for high speed and low latency.