Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint

Yingsong Li; Zhengxiong Jiang; Zhan Jin; Xiao Han; Jingwei Yin

doi:10.1109/ACCESS.2018.2867561

IEEE Access (Jan 2018)

Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint

Yingsong Li,
Zhengxiong Jiang,
Zhan Jin,
Xiao Han,
Jingwei Yin

Affiliations

Yingsong Li: ORCiD; College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Zhengxiong Jiang: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Zhan Jin: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Xiao Han: Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, China
Jingwei Yin: Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, China

DOI: https://doi.org/10.1109/ACCESS.2018.2867561
Journal volume & issue: Vol. 6
pp. 47794 – 47803

Abstract

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In this paper, an enhanced proportionate normalized least mean square (PNLMS) algorithm with the hybrid l2,0-norm constraint is proposed for block-sparse signal processing. The proposed algorithm penalizes a mixed l2,0-norm on the PNLMS to fully exploit the sparsity and handle block-sparse signals, which is called the l2,0 norm constrained PNLMS (L20-PNLMS). The L20-PNLMS is well derived and carefully analyzed. Various experiments have been constructed to verify the effectiveness of the devised L20-PNLMS. The experimental results demonstrate that the devised L20-PNLMS performs better than the previous PNLMS algorithms do in block sparse signal processing.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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