LHCAF-Based Hyperparameter-Free Sparse Channel Estimator for Hybrid MIMO Millimeter Wave Communication Under Impulsive Noise

Abstract

Multiple-input multiple-output (MIMO) millimeter wave (mmWave) is a promising technology for beyond 5 G communication systems, which provides high data rates, ultra-low latency, massive connectivity, and high spectral efficiency. However, a typical mmWave communication link faces major challenges due to signal loss over distance and imperfections in the hardware, which cause distortions that don’t follow the usual Gaussian pattern. Furthermore, MIMO mmWave channel exhibits sparseness due to blockage and scattering. The conventional orthogonal matching pursuit (OMP), zero-attracting least mean square (ZA-LMS), and their variants deliver suboptimal performance for MIMO-mmWave systems impaired by impulsive noise. In this letter, we propose robust zero-attracting logarithmic hyperbolic cosine adaptive filtering (ZA-LHCAF) for MIMO-mmWave channel estimation. Furthermore, we derive a sampling rule for spread parameter of ZA-LHCAF, which renders the proposed estimator hyperparameter free. The simulation results show that the proposed algorithm outperforms existing state-of-the-art estimators. Lastly, convergence analysis is presented for the proposed ZA-LHCAF algorithm, which validated by realistic computer simulations.