Impact of Practical Impairments on Adaptive Cascaded Channel Estimation for IRS-Aided mmWave Hybrid MIMO System

Abstract

Intelligent reflecting surfaces (IRS) combined with millimeter-wave (mmWave) MIMO systems represent a possible approach for improving coverage and capacity in next-generation wireless networks. Since mmWave signals suffer from severe path loss, beamforming is essential to concentrate energy in desired directions, and accurate channel state information (CSI) is required for efficient beamforming in these systems. However, standard channel estimation algorithms frequently assume Gaussian noise, which does not accurately reflect real-world conditions. This study analyses the performance of channel estimation under realistic impairments such as IRS phase noise, channel estimation errors, and impulsive noise. By incorporating these effects into the system model, we evaluate their impact on estimation accuracy and illustrate the limitations of ideal assumptions. The findings emphasise the importance of robust estimation methodologies and offer insights into the practical implementation of IRS-assisted mmWave systems. © 2025 IEEE.