Design of a Multi-User RIS-Aided System with Statistical Channel Knowledge

TL;DR

This paper proposes a multi-user RIS-aided system design leveraging statistical channel knowledge to reduce training overhead, using fractional programming and BCD methods for optimization, and enabling phase shifts to remain fixed over multiple intervals.

Contribution

It extends previous single-user RIS design to multi-user scenarios, introducing a novel optimization approach based on statistical channel info to reduce complexity and update frequency.

Findings

Achieves efficient multi-user RIS design with reduced training overhead.

Uses fractional programming and BCD for solving complex optimization.

Phase shifts based on statistics do not require frequent updates.

Abstract

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral and energy efficiency in a wireless communication system. The design of the phase shifts of an RIS in every channel coherence interval demands a huge training overhead, making its deployment practically infeasible. The design complexity can be significantly reduced by exploiting the second-order statistics of the channels. This paper is the extension of our previous work to the design of an RIS for the multi-user setup, where we employ maximisation of the lower bound of the achievable sum-rate of the users. Unlike for the single-user case, obtaining a closed-form expression for the update of the filters and phase shifts is more challenging in the multi-user case. We resort to the fractional programming (FP) approach and the non-convex block coordinate descent (BCD) method to solve the optimisation problem. As the phase shifts of the RIS obtained by the proposed algorithms are based on the statistical channel knowledge, they do not need to be updated in every channel coherence interval.