Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

Abstract

The new concept named reconfigurable intelligent surfaces (RIS) is becoming an appealing enabler due to its uniqueness with having low hardware complexity and low power consumption advantages simultaneously. In this paper, an RIS-aided vehicular Adhoc network (VANET) is considered, where the beacon vehicle is enabled with a passive RIS, the communication links between the beacon vehicle and client vehicle caused due to the multipath fading effects, are modeled with Fox’s H-function distribution. This paper first models the inter-vehicle links for the given system setup and then investigates the outage probability and effective rate as performance metrics. More specifically, the unsupervised expectation-maximization (EM) algorithm is consequently used to characterize the distribution of the received signal-to-noise ratio (SNR) at the client vehicle, which is modeled as the mixture of Gaussian (MoG) distribution. The accuracy of our approach is further validated with the Kolmogorov-Smirnov (KS) goodness of fit test. The MoG-based approach successfully tackles the RIS-enabled inter-vehicle communication with an easy, accurate, and tractable solution compared to the widely used central limit theorem (CLT) method. It leads to the closed-form outage probability and effective rate expressions.