Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation

TL;DR

This paper introduces enhanced sparse Bayesian learning methods, E-SBL and M-E-SBL, for massive MIMO channel estimation, demonstrating improved accuracy and computational efficiency over existing techniques through numerical simulations.

Contribution

The paper proposes two novel variants of sparse Bayesian learning, enhancing flexibility and efficiency for massive MIMO channel estimation, outperforming existing methods in accuracy and speed.

Findings

E-SBL and M-E-SBL outperform baseline SBL and VMP in mean squared error.

M-E-SBL achieves similar accuracy to E-SBL with lower computational cost.

Numerical results validate the effectiveness of the proposed methods.

Abstract

We consider the problem of sparse channel estimation in massive multiple-input multiple-output systems. In this context, we propose an enhanced version of the sparse Bayesian learning (SBL) framework, referred to as enhanced SBL (E-SBL), which is based on a reparameterization of the original SBL model. Specifically, we introduce a scale vector that brings extra flexibility to the model, which is estimated along with the other unknowns. Moreover, we introduce a variant of E-SBL, referred to as modified E-SBL (M-E-SBL), which is based on a computationally more efficient parameter estimation. We compare the proposed E-SBL and M-E-SBL with the baseline SBL and with a method based on variational message passing (VMP) in terms of computational complexity and performance. Numerical results show that the proposed E-SBL and M-E-SBL outperform the baseline SBL and VMP in terms of mean squared error of the channel estimation in all the considered scenarios. Furthermore, we show that M-E-SBL produces results comparable with E-SBL with considerably cheaper computations.