Asymptotically Near-Optimal Hybrid Beamforming for mmWave IRS-Aided MIMO Systems

Abstract

Hybrid beamforming is an emerging technique for massive multiple-input multiple-output (MIMO) systems due to the advantages of lower complexity, cost, and power consumption. Recently, intelligent reflection surface (IRS) has been proposed as the cost-effective technique for robust millimeter-wave (mmWave) MIMO systems. Thus, it is required to jointly optimize a reflection vector and hybrid beamforming matrices for IRS-aided mmWave MIMO systems. Due to the lack of RF chain in the IRS, it is unavailable to acquire the TX-IRS and IRS-RX channels separately. Instead, there are efficient methods to estimate the so-called effective (or cascaded) channel in the literature. In the large-system limit, we for the first time derive a near-optimal solution of the joint optimization, where it becomes an optimal solution under high signal-to-noise ratios (SNRs) and well-conditioned channels. Based on our theoretical analysis, we construct the practical reflection vector and hybrid beamforming matrices by enforcing the modulus constraints to the asymptotic solutions. Via simulations, it is demonstrated that the proposed method can outperform the state-of-the-art (SOTA) method, while the latter even requires the knowledge of the TX-IRS and IRS-RX channels separately. Furthermore, our construction can provide robustness against inevitable channel estimation errors in practical massive MIMO systems.