Can I read Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation on EtoBox?

Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation by Arjas, Arttu; Atzeni, Italo is a scholarly article available to read on EtoBox.

What is Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation about?

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.

Author

Arjas, Arttu; Atzeni, Italo

Published

2025

Language

EN