Improved Channel Estimation with Partial Sparse Constraint for AF Cooperative Communication Systems

TL;DR

This paper introduces an improved channel estimation method for AF cooperative communication systems that leverages partial sparse constraints, enhancing accuracy when the channel exhibits partial sparsity.

Contribution

The paper proposes a novel channel estimation approach using partial sparse constraints and LASSO, specifically designed for channels with partial sparsity, outperforming traditional methods.

Findings

Proposed method outperforms ordinary sparse estimation techniques.

Numerical simulations confirm improved accuracy.

Method effectively utilizes partial sparse structure.

Abstract

Accurate channel state information (CSI) is necessary for coherent detection in amplify and forward (AF) broadband cooperative communication systems. Based on the assumption of ordinary sparse channel, efficient sparse channel estimation methods have been investigated in our previous works. However, when the cooperative channel exhibits partial sparse structure rather than ordinary sparsity, our previous method cannot take advantage of the prior information. In this paper, we propose an improved channel estimation method with partial sparse constraint on cooperative channel. At first, we formulate channel estimation as a compressive sensing problem and utilize sparse decomposition theory. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over ordinary sparse channel estimation methods.