Distributed Compressive Sensing Based Doubly Selective Channel Estimation for Large-Scale MIMO Systems

TL;DR

This paper introduces a distributed compressive sensing approach for efficient doubly selective channel estimation in large-scale MIMO systems, reducing pilot overhead and complexity while improving accuracy.

Contribution

It proposes a novel DCS-based channel estimation scheme using BEM and a linear smoothing method, addressing pilot overhead and complexity issues in large-scale MIMO systems.

Findings

The scheme reduces pilot overhead significantly.

It achieves higher estimation accuracy than conventional methods.

Simulation results confirm performance improvements.

Abstract

Doubly selective (DS) channel estimation in largescale multiple-input multiple-output (MIMO) systems is a challenging problem due to the requirement of unaffordable pilot overheads and prohibitive complexity. In this paper, we propose a novel distributed compressive sensing (DCS) based channel estimation scheme to solve this problem. In the scheme, we introduce the basis expansion model (BEM) to reduce the required channel coefficients and pilot overheads. And due to the common sparsity of all the transmit-receive antenna pairs in delay domain, we estimate the BEM coefficients by considering the DCS framework, which has a simple linear structure with low complexity. Further more, a linear smoothing method is proposed to improve the estimation accuracy. Finally, we conduct various simulations to verify the validity of the proposed scheme and demonstrate the performance gains of the proposed scheme compared with conventional schemes.