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

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 alg...

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Main Authors: Yingsong Li, Zhan Jin, Yanyan Wang, Rui Yang
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:Entropy
Subjects:
LMS
Online Access:http://www.mdpi.com/1099-4300/18/10/380
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spelling doaj-a18f7833d81a415ca580f3a4fe804f3f2020-11-25T00:16:02ZengMDPI AGEntropy1099-43002016-10-01181038010.3390/e18100380e18100380A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel EstimationYingsong Li0Zhan Jin1Yanyan Wang2Rui Yang3College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaA 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.http://www.mdpi.com/1099-4300/18/10/380adaptive filtersLMSleast-mean mixed-normleast mean fourthbroadband multi-path sparse channel estimationcorrentropy-induced metric
collection DOAJ
language English
format Article
sources DOAJ
author Yingsong Li
Zhan Jin
Yanyan Wang
Rui Yang
spellingShingle Yingsong Li
Zhan Jin
Yanyan Wang
Rui Yang
A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation
Entropy
adaptive filters
LMS
least-mean mixed-norm
least mean fourth
broadband multi-path sparse channel estimation
correntropy-induced metric
author_facet Yingsong Li
Zhan Jin
Yanyan Wang
Rui Yang
author_sort Yingsong Li
title A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation
title_short A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation
title_full A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation
title_fullStr A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation
title_full_unstemmed A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation
title_sort robust sparse adaptive filtering algorithm with a correntropy induced metric constraint for broadband multi-path channel estimation
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2016-10-01
description 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.
topic adaptive filters
LMS
least-mean mixed-norm
least mean fourth
broadband multi-path sparse channel estimation
correntropy-induced metric
url http://www.mdpi.com/1099-4300/18/10/380
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