Robust Design for Intelligent Reflecting Surfaces Assisted MISO Systems

TL;DR

This paper proposes a robust beamforming design for IRS-assisted MISO systems that optimizes transmit and IRS parameters to minimize average MSE under Gaussian channel estimation errors, improving performance over traditional methods.

Contribution

It introduces a novel joint optimization algorithm for robust beamforming in IRS-assisted MISO systems considering imperfect CSI, using alternating optimization and majorization-minimization techniques.

Findings

Achieves robust MSE performance despite CSI errors

Outperforms conventional non-robust methods

Demonstrates effectiveness through simulations

Abstract

In this work, we study the statistically robust beamforming design for an intelligent reflecting surfaces (IRS) assisted multiple-input single-output (MISO) wireless system under imperfect channel state information (CSI), where the channel estimation errors are assumed to be additive Gaussian. We aim at jointly optimizing the transmit/receive beamformers and IRS phase shifts to minimize the average mean squared error (MSE) at the user. In particular, to tackle the non-convex optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization and majorization-minimization techniques. Simulation results show that the proposed scheme achieves robust MSE performance in the presence of CSI error, and substantially outperforms conventional non-robust methods.