Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

TLDR

Reconfigurable intelligent surfaces enable a programmable wireless environment by steering incident signals through reconfigurable passive elements. This study investigates a RIS‑aided multiuser MISO downlink system. The authors jointly optimize access‑point beamforming and RIS phase shifts to maximize weighted sum‑rate, proposing a low‑complexity fractional‑programming algorithm for perfect CSI and extending it with stochastic successive convex approximation for imperfect CSI. Numerical results validate the approach, showing the algorithm performs well when channel uncertainty is below 10 %.

Abstract

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.