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Entropy 2016, 18(10), 380; doi:10.3390/e18100380

A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation

1
College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China
2
College of Communication and Information Engineering, Qiqihar University, Qiqihar 161006, China
3
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Academic Editor: Dawn E. Holmes
Received: 27 August 2016 / Revised: 15 October 2016 / Accepted: 20 October 2016 / Published: 24 October 2016
(This article belongs to the Special Issue Maximum Entropy and Its Application II)
View Full-Text   |   Download PDF [3004 KB, uploaded 24 October 2016]   |  

Abstract

A robust sparse least-mean mixture-norm (LMMN) algorithm is proposed, and its performance is appraised in the context of estimating a broadband multi-path wireless channel. The proposed algorithm is implemented via integrating a correntropy-induced metric (CIM) penalty into the conventional LMMN algorithm to modify the basic cost function, which is denoted as the CIM-based LMMN (CIM-LMMN) algorithm. The proposed CIM-LMMN algorithm is derived in detail within the kernel framework. The updating equation of CIM-LMMN can provide a zero attractor to attract the non-dominant channel coefficients to zeros, and it also gives a tradeoff between the sparsity and the estimation misalignment. Moreover, the channel estimation behavior is investigated over a broadband sparse multi-path wireless channel, and the simulation results are compared with the least mean square/fourth (LMS/F), least mean square (LMS), least mean fourth (LMF) and the recently-developed sparse channel estimation algorithms. The channel estimation performance obtained from the designated sparse channel estimation demonstrates that the CIM-LMMN algorithm outperforms the recently-developed sparse LMMN algorithms and the relevant sparse channel estimation algorithms. From the results, we can see that our CIM-LMMN algorithm is robust and is superior to these mentioned algorithms in terms of both the convergence speed rate and the channel estimation misalignment for estimating a sparse channel. View Full-Text
Keywords: adaptive filters; LMS; least-mean mixed-norm; least mean fourth; broadband multi-path sparse channel estimation; correntropy-induced metric adaptive filters; LMS; least-mean mixed-norm; least mean fourth; broadband multi-path sparse channel estimation; correntropy-induced metric
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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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Li, Y.; Jin, Z.; Wang, Y.; Yang, R. A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation. Entropy 2016, 18, 380.

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