An information-theoretic branch-and-prune algorithm for discrete phase optimization of RIS in massive MIMO

TL;DR

This paper introduces an innovative information-theoretic branch-and-prune algorithm for optimizing RIS phase settings in massive MIMO systems, improving communication performance by minimizing MSE and maximizing throughput.

Contribution

It presents the first use of an information-theoretic measure in a branch-and-prune algorithm for RIS phase optimization, with theoretical guarantees of near-optimality.

Findings

The proposed IDBP algorithm achieves near-optimal RIS phase settings.

Minimizing MSE aligns with maximizing throughput and energy efficiency.

Theoretical guarantees confirm near-optimal performance of the solution.

Abstract

In this paper, we consider passive RIS-assisted multi-user communication between wireless nodes to improve the blocked line-of-sight (LOS) link performance. The wireless nodes are assumed to be equipped with Massive Multiple-Input Multiple-Output antennas, hybrid precoder, combiner, and low-resolution analog-to-digital converters (ADCs). We first derive the expression for the Cramer-Rao lower bound (CRLB) of the Mean Squared Error (MSE) of the received and combined signal at the intended receiver under interference. By appropriate design of the hybrid precoder, combiner, and RIS phase settings, it can be shown that the MSE achieves the CRLB. We further show that minimizing the MSE w.r.t. the phase settings of the RIS is equivalent to maximizing the throughput and energy efficiency of the system. We then propose a novel Information-Directed Branch-and-Prune (IDBP) algorithm to derive the phase settings of the RIS. We, for the first time in the literature, use an information-theoretic measure to decide on the pruning rules in a tree-search algorithm to arrive at the RIS phase-setting solution, which is vastly different compared to the traditional branch-and-bound algorithm that uses bounds of the cost function to define the pruning rules. In addition, we provide the theoretical guarantees of the near-optimality of the RIS phase-setting solution thus obtained using the Asymptotic Equipartition property. This also ensures near-optimal throughput and MSE performance.