Efficient Coordinated Recovery of Sparse Channels in Massive MIMO

TL;DR

This paper introduces a novel, coordinated Bayesian channel estimation method for massive MIMO-OFDM systems that exploits sparsity and common support to improve accuracy and reduce pilot requirements.

Contribution

It proposes two algorithms leveraging sparsity and shared support among antennas for efficient, low-pilot channel estimation in massive MIMO systems.

Findings

Improved channel estimation accuracy over traditional methods

Reduced pilot overhead in massive MIMO systems

Enhanced performance with data-aided algorithm version

Abstract

This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and require a small number of pilots. Two algorithms based on this approach have been developed which perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.