Adaptive Sparse Aware Algorithm based Channel Estimation for mmWave Hybrid MIMO System

Abstract

Millimeter-wave (mmWave) MIMO has emerged as a promising technology that offers improved spectral efficiency and enhanced data rates for 5G and beyond 5G (B5G) wireless networks. However, the achievement of this high spectral efficiency and high data-rates is subject to accurate channel-estimation, which in-general, is challenging for mmWave MIMO due to scattering and blockages. These factors lead to inherent sparsity in mmWave MIMO channel, which in-turn necessitates sparse-aware channel-estimation methods. Therefore, this paper proposes a zero-attracting least mean squares (ZA-LMS) based channel-estimator and analyzes its convergence. The proposed ZA-LMS based channel-estimator exploits the inherent sparsity of the overall channel-matrix, that in-turn, leads to significantly faster convergence. These benefits of the proposed sparse channel-estimation algorithm and its convergence are illustrated through computer simulations over typical mmWave MIMO channel. © 2021 IEEE.