Beamspace channel estimation in wideband lens antenna array-based mmWave mMIMO-OFDM systems under beam squint

Abstract

Sparse Bayesian learning (SBL) and particularly relevant vector machines (RVMs) have drawn much attention to improving the performance of existing machine learning models. The methodology depends on a parameterized prior that enforces models with weight sparsity, where only a few are non-zeros. Wideband mmWave massive multiple-input multiple-output (mMIMO) systems with lens antenna array (LAA), expect to play a key role in future fifth-generation (5G) wireless systems. To provide the beamforming gain required to overcome path loss, we consider a lens antenna array (LAA)-based beamspace mMIMO system. However, the spatial-wideband influence causes the beam squint effect to emerge, making the beamspace channel path components exhibit a unique frequency-dependent sparse structure, and thus nullifies the frequency domain common support assumption. In this paper, we first propose a channel estimation (CE) algorithm, namely a reduced-antenna selection progressive support-detection (RAS-PSD), for the wideband mmWave mMIMO-OFDM systems with LAA, which considers the beam squint effect. Secondly, by exploring Bayesian learning (BL), a Gaussian Process hyperparameter optimization-based CE (GP-HOCE) algorithm is proposed for the considered system, where both its own hyperparameter and the hyperparameters of its adjacent neighbors governs the sparsity of each coefficient. The simulation results show that the wideband beamspace channel coefficients can be estimated more efficiently than those of the existing state-of-the-art algorithms in terms of normalized mean square error (NMSE) of CE for wideband mmWave mMIMO-OFDM systems.