Performance Analysis of RIS-Assisted Large-Scale Wireless Networks Using Stochastic Geometry

TL;DR

This paper analyzes the performance of large-scale RIS-assisted wireless networks using stochastic geometry, deriving coverage probability and rate expressions, and demonstrating the benefits of passive beamforming in interference-limited scenarios.

Contribution

It introduces a stochastic geometry framework for RIS-assisted networks considering spatial correlation and derives closed-form expressions for key performance metrics.

Findings

RIS-assisted transmission improves coverage and rate.

Performance is independent of transmitter density with nearest association.

Passive beamforming significantly enhances system performance.

Abstract

In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted large-scale network by characterizing the coverage probability and the average achievable rate using stochastic geometry. Considering the spatial correlation between transmitters (TXs) and RISs, their locations are jointly modelled by a Gauss-Poisson process (GPP). Two association strategies, i.e., nearest association and fixed association, are both discussed. For the RIS-aided transmission, the signal power distribution with a direct link is approximated by a gamma random variable using a moment matching method, and the Laplace transform of the aggregate interference power is derived in closed form. Based on these expressions, we analyze the channel hardening effect in the RIS-assisted transmission, the coverage probability, and the average achievable rate of the typical user. We derive the coverage probability expressions for the fixed association strategy and the nearest association strategy in an interference-limited scenario in closed form. Numerical results are provided to validate the analysis and illustrate the effectiveness of RIS-assisted transmission with passive beamforming in improving the system performance. Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.