Low-Complexity Message Passing Based Massive MIMO Channel Estimation by Exploiting Unknown Sparse Common Support with Dirichlet Process

TL;DR

This paper introduces a low-complexity message passing Bayesian method leveraging Dirichlet processes to estimate sparse massive MIMO channels with unknown common support, outperforming existing algorithms.

Contribution

It proposes a novel Bayesian channel estimation technique using Dirichlet processes and message passing, addressing unknown cluster support in massive MIMO systems.

Findings

Outperforms state-of-the-art algorithms in simulations.

Achieves better performance than variational Bayesian methods.

Effective in estimating channels with unknown sparse support.

Abstract

This paper investigates the problem of estimating sparse channels in massive MIMO systems. Most wireless channels are sparse with large delay spread, while some channels can be observed having sparse common support (SCS) within a certain area of the antenna array, i.e., the antenna array can be grouped into several clusters according to the sparse supports of channels. The SCS property is attractive when it comes to the estimation of large number of channels in massive MIMO systems. Using the SCS of channels, one expects better performance, but the number of clusters and the elements for each cluster are always unknown in the receiver. In this paper, {the Dirichlet process} is exploited to model such sparse channels where those in each cluster have SCS. We proposed a low complexity message passing based sparse Bayesian learning to perform channel estimation in massive MIMO systems by using combined BP with MF on a factor graph. Simulation results demonstrate that the proposed massive MIMO sparse channel estimation outperforms the state-of-the-art algorithms. Especially, it even shows better performance than the variational Bayesian method applied for massive MIMO channel estimation.