Sparse Channel Estimation for Amplify-and-Forward Two-way Relay Network with Compressed Sensing

TL;DR

This paper introduces a sparse channel estimation method for amplify-and-forward two-way relay networks that leverages the sparse nature of wireless channels, significantly improving estimation accuracy over traditional methods.

Contribution

The paper proposes a novel sparse channel estimation technique utilizing compressed sensing for AF-TWRN, enhancing spectral efficiency and MSE performance compared to conventional linear estimation.

Findings

Significant MSE improvement over LS-based methods

Effective exploitation of channel sparsity in simulations

Enhanced spectral efficiency in wireless relay networks

Abstract

Amplify-and-forward two-way relay network (AFTWRN) was introduced to realize high-data rate transmission over the wireless frequency-selective channel. However, AFTWRC requires the knowledge of channel state information (CSI) not only for coherent data detection but also for the selfdata removal. This is partial accomplished by training sequence-based linear channel estimation. However, conventional linear estimation techniques neglect anticipated sparsity of multipath channel and thus lead to low spectral efficiency which is scarce in the field of wireless communication. Unlike the previous methods, we propose a sparse channel estimation method which can exploit the sparse structure and hence provide significant improvements in MSE performance when compared with traditional LS-based linear channel probing strategies in AF-TWRN. Simulation results confirm the proposed methods.