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Algorithms 2015, 8(4), 799-809; doi:10.3390/a8040799

Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments

1
School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2
College of Computer Science, Chongqing University, Chongqing 400044, China
3
Department Electronics and Information Systems, Akita Prefectural University, Akita 015-0055, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Paul M. Goggans
Received: 6 July 2015 / Revised: 2 September 2015 / Accepted: 11 September 2015 / Published: 25 September 2015
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Abstract

The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors’ knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths. View Full-Text
Keywords: Sign least mean square (SLMS); reweighted L1-norm (RL1); reweighted factor selection; Gaussian mixture model (GMM); sparse channel estimation Sign least mean square (SLMS); reweighted L1-norm (RL1); reweighted factor selection; Gaussian mixture model (GMM); sparse channel estimation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, T.; Gui, G. Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments. Algorithms 2015, 8, 799-809.

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