Compressive Channel Estimation for Two-way Relay Network in a Frequency-Selective Channel with Compressed Sensing

TL;DR

This paper introduces a compressive sensing-based channel estimation method for two-way relay networks in frequency-selective channels, exploiting sparsity to improve mean squared error performance over traditional linear methods.

Contribution

It presents a novel compressive channel estimation technique that leverages the sparse structure of multipath channels in TWRNs, outperforming conventional linear estimation methods.

Findings

Significant MSE performance improvements over traditional methods

Simulation results validate the effectiveness of the proposed approach

Exploiting sparsity enhances channel estimation accuracy

Abstract

Two-way relay network (TWRN) was introduced to realize high-data rate transmission over the wireless frequency-selective channel. However, TWRC requires the knowledge of channel state information (CSI) not only for coherent data detection but also for the self-data removal. This is partial accomplished by training sequence-based linear channel estimation. However, conventional linear estimation techniques neglect anticipated sparsity of multipath channel. Unlike the previous methods, we propose a compressive channel estimation method which exploit the sparse structure and provide significant improvements in MSE performance when compared with traditional LSbased linear channel probing strategies. Simulation results confirm the proposed methods.