A Distributed Sparse Channel Estimation Technique for mmWave Massive MIMO Systems

TL;DR

This paper introduces WDiOMP, a distributed algorithm for sparse channel estimation in mmWave massive MIMO systems that collaboratively estimates support sets, improving accuracy over existing methods in multi-user scenarios.

Contribution

The paper proposes a novel distributed sparse channel estimation method, WDiOMP, that leverages common sparsity structures and outperforms existing centralized and local algorithms.

Findings

WDiOMP achieves lower support set recovery error than DiOMP, local OMP, and SOMP.

The algorithm performs well even when the underlying sparsity structure is unknown.

Distributed approach improves estimation accuracy in multi-user mmWave MIMO systems.

Abstract

In this paper, we study the problem of sparse channel estimation via a collaborative and fully distributed approach. The estimation problem is formulated in the angular domain by exploiting the spatially common sparsity structure of the involved channels in a multi-user scenario. The sparse channel estimation problem is solved via an efficient distributed approach in which the participating users collaboratively estimate their channel sparsity support sets, before locally estimate the channel values, under the assumption that global and common support subsets are present. The performance of the proposed algorithm, named WDiOMP, is compared to DiOMP, local OMP and a centralized solution based on SOMP, in terms of the support set recovery error under various experimental scenarios. The efficacy of WDiOMP is demonstrated even in the case in which the underlining sparsity structure is unknown.