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

by 1,2,* and 3
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
Algorithms 2015, 8(4), 799-809; https://doi.org/10.3390/a8040799
Received: 6 July 2015 / Revised: 2 September 2015 / Accepted: 11 September 2015 / Published: 25 September 2015
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
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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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