Interference Mitigation in STAR-RIS-Aided Multi-User Networks with Statistical CSI

TL;DR

This paper proposes a Riemannian manifold-based conjugate gradient algorithm for real-time interference mitigation in STAR-RIS-assisted multi-user networks using only statistical CSI, accounting for hardware imperfections.

Contribution

It introduces a novel approximation of the effective channel and a manifold optimization approach for interference minimization with limited CSI and phase errors.

Findings

Significant interference suppression demonstrated in simulations

Improved SINR performance over baseline methods

Faster convergence with the proposed algorithm

Abstract

In this paper, we investigate real-time interference mitigation in multiuser wireless networks assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). Unlike conventional methods that rely on instantaneous channel state information (CSI), we consider a practical scenario where only statistical CSI is available, and the STAR-RIS phase shifts are impaired by random phase errors modeled via the Von Mises distribution. To tackle the resulting nonconvex optimization problem induced by unit-modulus constraints and stochastic interference, we derive a closed-form approximation of the effective channel matrix using statistical expectations. We then reformulate the interference minimization problem as an unconstrained optimization over a Riemannian manifold and propose a conjugate gradient algorithm tailored to the complex circle manifold. The proposed solution enables efficient real-time computation of optimal phase shifts while accounting for hardware imperfections and limited CSI. Simulation results confirm that our method significantly suppresses inter-user interference and achieves superior SINR performance and convergence speed compared to conventional baselines.