Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System

A viable technology for the future wireless communication system to obtain extremely high information rates with improved coverage is the collaborative incorporation of an intelligent reflecting surface (IRS) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. An IRS provides a virtual line-of-sight (LoS) path to enhance the wireless system’s capacity. However, accurate channel state information is essential for the complete utilization of IRS and mmWave MIMO systems. Existing channel estimators based on orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL) entail large pilot overhead and matrix inversion. Therefore, these techniques offer low spectral efficiency and high computational complexity. To overcome the limitations of existing estimators, we propose an online variable step-size zero-attracting least mean square (VSS-ZALMS) based algorithm for IRS-assisted mmWave hybrid MIMO system channel estimation. Further, we derive analytical expressions for the range of step-size and regularization parameters to improve estimation accuracy and convergence rates. Moreover, we conduct an analysis of IRS location, spectral efficiency, complexity analysis, and pilot overhead requirements. Simulation results are then compared with OMP, SBL, and oracle least square for benchmarking. The results corroborate superiority of the proposed approach concerning accuracy, complexity, and robustness compared to the existing estimators.