Sparse Multipath Channel Estimation using DS Algorithm in Wideband Communication Systems

TL;DR

This paper introduces a novel compressed sensing-based method using the Dantzig selector for sparse multipath channel estimation in wideband wireless systems, improving spectral efficiency by exploiting channel sparsity.

Contribution

The paper proposes a new DS-based sparse channel estimation method that reduces training sequence length and enhances spectral efficiency over traditional methods.

Findings

Reduces training sequence length

Increases spectral efficiency

Outperforms existing methods in simulations

Abstract

Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. They are often called sparse multi-path channels (MPC). Conventional linear MPC methods, such as the least squares (LS), do not exploit the sparsity of MPC. In general, accurate sparse MPC estimator can be obtained by solving a LASSO problem even in the presence of noise. In this paper, a novel CS-based sparse MPC method by using Dantzig selector (DS) [1] is introduced. This method exploits a channel's sparsity to reduce the number of training sequence and, hence, increase spectral efficiency when compared to existed methods with computer simulations.