Nonlinear Sparse Channel Estimator for Hybrid MIMO Millimeter Wave Communication

The multi-input multi-output (MIMO) millimeter wave (mmWave) is a promising technology for beyond 5G communication systems, which provides high data rates, ultra-low latency, massive connectivity, and high spectral efficiency. However, a typical mmWave communication link is severely impaired by path loss, nonlinear device impairments, and non-Gaussian noise. 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 nonlinear mmWave systems. To improve the estimation accuracy, we propose a random Fourier features (RFF) based sparsity-aware kernel maximum correntropy (SA-KMC) algorithm, which is robust for MIMO mmWave sparse channel estimation in the presence of nonlinear hardware impairments and non-Gaussian distortions. Simulations indicate that the proposed RFF-SA-MCC algorithm outperforms the existing state-of-art approaches.