Robust Resource Allocation for RIS-aided V2X Communications with Imperfect CSI

This paper investigates a robust resource allocation for reconfigurable intelligent surface (RIS) aided vehicle-to-everything (V2X) communications with imperfect channel state information (CSI). To satisfy the diverse quality-of-service (QoS) requirements of V2X communications, we aim at maximizing the sum capacity of cellular user equipments (CUEs) while guaranteeing the outage probability constraints of vehicular user equipments (VUEs). Then, the considered problem is decomposed into the subproblems of power, spectrum and RIS phase shift optimization. A graph-based power allocation method is presented to transform the non-convex power allocation subproblem into a tractable one and obtain the closed-form solutions. A worst-case conditional value-at-risk (CVaR) approximation-based method is developed to convert the RIS phase optimization subproblem into a convex semidefinite programming (SDP) problem. We propose a low-complexity learning-based alternating optimization approach which alternately optimizes three subproblems to obtain a near-optimal solution. Simulation results demonstrate that the proposed approach outperforms other benchmark methods.