An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System

An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System

In this paper, an improved set-membership proportionate normalized least mean square (SM-PNLMS) algorithm is proposed for block-sparse systems. The proposed algorithm, which is named the block-sparse SM-PNLMS (BS-SMPNLMS), is implemented by inserting a penalty of a mixed l 2 , 1 norm of we...

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Main Authors: Zhan Jin, Yingsong Li, Jianming Liu
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Symmetry
Subjects:
set-membership principle
PNLMS algorithm
block-sparse system
Online Access:http://www.mdpi.com/2073-8994/10/3/75
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spelling doaj-bf29da54ec29407abd5e3c72c19ef6d72020-11-24T23:04:17ZengMDPI AGSymmetry2073-89942018-03-011037510.3390/sym10030075sym10030075An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse SystemZhan Jin0Yingsong Li1Jianming Liu2College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaTencent AI Lab, Bellevue, WA 98004, USAIn this paper, an improved set-membership proportionate normalized least mean square (SM-PNLMS) algorithm is proposed for block-sparse systems. The proposed algorithm, which is named the block-sparse SM-PNLMS (BS-SMPNLMS), is implemented by inserting a penalty of a mixed l 2 , 1 norm of weight-taps into the cost function of the SM-PNLMS. Furthermore, an improved BS-SMPNLMS algorithm (the (BS-SMIPNLMS algorithm) is also derived and analyzed. The proposed algorithms are well investigated in the framework of network echo cancellation. The results of simulations indicate that the devised BS-SMPNLMS and BS-SMIPNLMS algorithms converge faster and have smaller estimation errors compared with related algorithms.http://www.mdpi.com/2073-8994/10/3/75set-membership principlePNLMS algorithmblock-sparse system
collection DOAJ
language English
format Article
sources DOAJ
author Zhan Jin
Yingsong Li
Jianming Liu
spellingShingle Zhan Jin
Yingsong Li
Jianming Liu
An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
Symmetry
set-membership principle
PNLMS algorithm
block-sparse system
author_facet Zhan Jin
Yingsong Li
Jianming Liu
author_sort Zhan Jin
title An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
title_short An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
title_full An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
title_fullStr An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
title_full_unstemmed An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
title_sort improved set-membership proportionate adaptive algorithm for a block-sparse system
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2018-03-01
description In this paper, an improved set-membership proportionate normalized least mean square (SM-PNLMS) algorithm is proposed for block-sparse systems. The proposed algorithm, which is named the block-sparse SM-PNLMS (BS-SMPNLMS), is implemented by inserting a penalty of a mixed l 2 , 1 norm of weight-taps into the cost function of the SM-PNLMS. Furthermore, an improved BS-SMPNLMS algorithm (the (BS-SMIPNLMS algorithm) is also derived and analyzed. The proposed algorithms are well investigated in the framework of network echo cancellation. The results of simulations indicate that the devised BS-SMPNLMS and BS-SMIPNLMS algorithms converge faster and have smaller estimation errors compared with related algorithms.
topic set-membership principle
PNLMS algorithm
block-sparse system
url http://www.mdpi.com/2073-8994/10/3/75
work_keys_str_mv AT zhanjin animprovedsetmembershipproportionateadaptivealgorithmforablocksparsesystem
AT yingsongli animprovedsetmembershipproportionateadaptivealgorithmforablocksparsesystem
AT jianmingliu animprovedsetmembershipproportionateadaptivealgorithmforablocksparsesystem
AT zhanjin improvedsetmembershipproportionateadaptivealgorithmforablocksparsesystem
AT yingsongli improvedsetmembershipproportionateadaptivealgorithmforablocksparsesystem
AT jianmingliu improvedsetmembershipproportionateadaptivealgorithmforablocksparsesystem
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