Hardware-Impaired Beamforming Optimization for Cooperative Double-IRS Aided Wireless Communications

Abstract

Intelligent reflecting surface (IRS) has been regarded as an innovative technology to enhance wireless communications. Most existing IRS-related works have focused on the ideal hardware conditions. More practically, in this work, we investigate a double-IRS aided multiple-input multiple-output (MIMO) communication system with inevitable transceiver hardware impairments (HWIs). Specifically, we aim to minimize the mean-square-error (MSE) of the downlink transmission by jointly optimizing the transceiver beamforming matrices and the IRS reflection coefficients. In order to achieve lower hardware overhead and signal processing complexity, we assume that the two IRSs share the common reflection coefficients, which usually leads to a more challenging MSE minimization problem due to the intractable quartic term. To tackle this intractability effectively, we propose a majorization-minimization (MM) based alternating optimization (AO) algorithm. For the sake of low computational complexity, we also develop a two-stage algorithm. Finally, simulation results demonstrate that the proposed MM-based AO algorithm achieves comparable performance to the case of double-distinct-IRS scheme, while the two-stage algorithm can strike a balance between the complexity and the system performance.