Resource Allocation for Intelligent Reflecting Surface Aided Vehicular Communications
Yuanbin Chen, Ying Wang, Jiayi Zhang, Zhendong Li
Abstract
This article studies the resource allocation for intelligent reflecting surface aided vehicular communications based on slowly varying large-scale fading channel information. To meet different quality-of-service (QoS) requirements of vehicular communications, we aim to maximize the sum vehicle-to-infrastructure (V2I) link capacity while guaranteeing the minimum signal-to-interference-plus-noise ratio (SINR) of vehicle-to-vehicle (V2V) links. More specifically, the power allocation, IRS reflection coefficients and spectrum allocation are jointly optimized. To tackle the formulated mixed integer non-convex problem, we divide it into two stages, which yields an alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm and the IRS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity.