Sparse Multipath Channel Estimation using DS Algorithm in Wideband Communication Systems
Guan Gui, An-min Huang, Qun Wan

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.
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Advanced Adaptive Filtering Techniques · Blind Source Separation Techniques
