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Open AccessArticle

Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian 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
Institute of Signal Transmission and Processing, College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Paul M. Goggans
Algorithms 2016, 9(3), 54; https://doi.org/10.3390/a9030054
Received: 24 June 2016 / Revised: 26 July 2016 / Accepted: 9 August 2016 / Published: 12 August 2016
Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results. View Full-Text
Keywords: robust sparse channel estimation; sign function based least mean square error (SLMS); sparsity-promoting function; non-Gaussian noise; convergence analysis robust sparse channel estimation; sign function based least mean square error (SLMS); sparsity-promoting function; non-Gaussian noise; convergence analysis
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Zhang, T.; Gui, G. Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments. Algorithms 2016, 9, 54.

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