Optimum Reconfigurable Intelligent Surface Selection for Wireless Networks

TL;DR

This paper explores optimal location-based RIS selection strategies in wireless networks to enhance signal quality, proposing distributed methods and analyzing their performance through outage probabilities and average rates.

Contribution

It introduces novel RIS selection policies tailored for product and sum path-loss models, including a limited-feedback framework for distributed operation.

Findings

Proposed RIS selection policies significantly improve network performance.

Derived analytical expressions for outage probabilities and average rates.

Numerical results confirm notable performance gains of the proposed methods.

Abstract

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with the product and sum of the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies.