Bayesian Learning Aided Simultaneous Sparse Estimation of Dual-Wideband THz Channels in Multi-User Hybrid MIMO Systems

TL;DR

This paper introduces a Bayesian group-sparse regression method for estimating dual-wideband THz channels in multi-user hybrid MIMO systems, accounting for realistic channel models and hardware constraints.

Contribution

The work develops a practical dual-wideband THz channel model and proposes a novel BGSR framework for sparse channel estimation with low-resolution ADCs.

Findings

BGSR outperforms existing sparse estimation methods in NMSE and BER.

The proposed model accurately captures absorption, reflection, and diffuse scattering effects.

The Bayesian Cramér-Rao Bound provides a theoretical performance benchmark.

Abstract

This work conceives the Bayesian Group-Sparse Regression (BGSR) for the estimation of a spatial and frequency wideband, i.e., a dual wideband channel in Multi-User (MU) THz hybrid MIMO scenarios. We develop a practical dual wideband THz channel model that incorporates absorption losses, reflection losses, diffused ray modeling and angles of arrival/departure (AoAs/AoDs) using a Gaussian Mixture Model (GMM). Furthermore, a low-resolution analog-to-digital converter (ADC) is employed at each RF chain, which is crucial for wideband THz massive MIMO systems to reduce power consumption and hardware complexity, given the high sampling rates and large number of antennas involved. The quantized MU THz MIMO model is linearized using the popular Bussgang decomposition followed by BGSR based channel learning framework that results in sparsity across different subcarriers, where each subcarrier has its unique dictionary matrix. Next, the Bayesian Cram\'er Rao Bound (BCRB) is devised for bounding the normalized mean square error (NMSE) performance. Extensive simulations were performed to assess the performance improvements achieved by the proposed BGSR method compared to other sparse estimation techniques. The metrics considered for quantifying the performance improvements include the NMSE and bit error rate (BER).