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Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations
Open AccessArticle

Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels

1
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
3
Department of Electronics, Valahia University of Targoviste, Targoviste 130082, Romania
4
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(8), 432; https://doi.org/10.3390/e19080432
Received: 6 July 2017 / Revised: 16 August 2017 / Accepted: 18 August 2017 / Published: 22 August 2017
A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of the compressive sensing (CS) concept and zero attracting (ZA) techniques and its estimating behavior is verified over sparse multi-path channels. The proposed algorithm is implemented by exerting different norm penalties on the two grouped channel coefficients to improve the channel estimation performance in a mixed noise environment. As a result, a zero attraction term is obtained from the expected l 0 and l 1 penalty techniques. Furthermore, a reweighting factor is adopted and incorporated into the zero-attraction term of the GC-MCC algorithm which is denoted as the reweighted GC-MCC (RGC-MMC) algorithm to enhance the estimation performance. Both the GC-MCC and RGC-MCC algorithms are developed to exploit well the inherent sparseness properties of the sparse multi-path channels due to the expected zero-attraction terms in their iterations. The channel estimation behaviors are discussed and analyzed over sparse channels in mixed Gaussian noise environments. The computer simulation results show that the estimated steady-state error is smaller and the convergence is faster than those of the previously reported MCC and sparse MCC algorithms. View Full-Text
Keywords: sparse MCC algorithms; mixed noise environment; zero-attracting technique; norm penalties sparse MCC algorithms; mixed noise environment; zero-attracting technique; norm penalties
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Wang, Y.; Li, Y.; Albu, F.; Yang, R. Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels. Entropy 2017, 19, 432.

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