Hyperparameter Free Information Theoretic Learning Based Channel Estimation for mmWave MIMO

Abstract

MmWave MIMO (millimeter wave multiple-input multiple-output) systems are critical for enabling the efficient use of spectrum and ultra-fast transmission speeds required by 5G and beyond 5G wireless networks. However, accurate channel-estimation over mmWave MIMO systems is challenging due to presence of impulsive noise due to environmental factors, which significantly degrades performance of classical Bussgang/mean-squared based channel-estimation methods. To mitigate signal impairments due to unknown non-Gaussian noise processes, this work proposes novel information theoretic learning (ITL) based sparse channel estimation algorithms, namely, zero attracting MCC, and Hyperparameter-Free zero-attracting MCC (ZAMCC). These ITL based algorithms exploit the sparse nature of mm Wave MIMO channels and mitigate the adverse effects of impulsive noise. Computer simulations are presented for the performance evaluation for the proposed ITL based algorithms assuming realistic mmWave MIMO scenarios. From the simulations, it is inferred that the proposed ITL based algorithms are promising for accurate hyperparameter-free channel-estimation for practical mmWave MIMO channels impaired by unknown non-Gaussian noises. © 2024 IEEE.