Resource allocation for reconfigurable intelligent surface aided broadcast channels

TL;DR

This paper investigates resource allocation in a two-user downlink network with reconfigurable intelligent surfaces, proposing new approximation models and algorithms to optimize signal quality and sum rate, demonstrating improved performance over existing methods.

Contribution

It introduces novel approximation models and algorithms for joint optimization of precoding and RIS configuration in RIS-assisted broadcast channels.

Findings

Proposed algorithms converge to KKT points.

Simulation shows better performance than existing algorithms.

Derived upper bounds improve optimization effectiveness.

Abstract

A two-user downlink network aided by a reconfigurable intelligent surface is considered. The weighted sum signal to interference plus noise ratio maximization and the sum rate maximization models are presented, where the precoding vectors and the RIS matrix are jointly optimized. Since the optimization problem is non-convex and difficult, new approximation models are proposed. The upper bounds of the corresponding objective functions are derived and maximized. Two new algorithms based on the alternating direction method of multiplier are proposed. It is proved that the proposed algorithms converge to the KKT points of the approximation models as long as the iteration points converge. Simulation results show the good performances of the proposed models compared to state of the art algorithms.