Sparse Channel Estimation for Hybrid MIMO Millimeter Wave Communication Impaired by Impulsive Noise

Multiple-input multiple-output (MIMO) millimeter wave (mmWave) technology is a key enabler for beyond 5G communication systems, offering high data rates, ultra-low latency, massive connectivity, and enhanced spectral efficiency. However, the performance of a MIMO-mmWave communication link is degraded by path loss and non-Gaussian/impulsive distortions. Furthermore, blockage and scattering effects make the overall MIMO-mmWave channel sparse in nature. The existing orthog-onal matching pursuit (OMP), zero-attracting least mean square (ZA-LMS), and their variants are inadequate to achieve near-optimal performance for mmWave systems impaired by impulsive noise. To circumvent this limitation, we propose a novel sparsity-aware maximum Versoria criterion (SA-MVC) based channel estimator for MIMO-mmWave communication, which is robust in the presence of impulsive/ non-Gaussian distortions. Simulations conducted on a realistic MIMO-mmWave channel demonstrate that the proposed SA-MV C algorithm delivers better convergence performance over the existing state-of-art methods.