Sparse Channel Estimation for MIMO-OFDM Amplify-and-Forward Two-Way Relay Networks

TL;DR

This paper introduces a sparse channel estimation technique for MIMO-OFDM AF-TWRN that leverages compressed sensing and LASSO to improve accuracy over traditional least squares methods.

Contribution

The paper proposes a novel sparse channel estimation method using compressed sensing and LASSO for MIMO-OFDM AF-TWRN, exploiting channel sparsity for better performance.

Findings

Proposed method outperforms LS-based estimation in simulations.

Exploiting channel sparsity improves estimation accuracy.

LASSO algorithm effectively implements sparse channel estimation.

Abstract

Accurate channel impulse response (CIR) is required for coherent detection and it can also help improve communication quality of service in next-generation wireless communication systems. One of the advanced systems is multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) amplify and forward two-way relay networks (AF-TWRN). Linear channel estimation methods, e.g., least square (LS), have been proposed to estimate the CIR. However, these methods never take advantage of channel sparsity and then cause performance loss. In this paper, we propose a sparse channel estimation method to exploit the sparse structure information in the CIR at each end user. Sparse channel estimation problem is formulated as compressed sensing (CS) using sparse decomposition theory and the estimation process is implemented by LASSO algorithm. Computer simulation results are given to confirm the superiority of proposed method over the LS-based channel estimation method.