Joint adaptive step-size and zero-attractor parameters for l0-NLMS algorithm

Sheng Zhang; Wei Xing Zheng

doi:10.1109/ISCAS.2018.8351489

Joint adaptive step-size and zero-attractor parameters for l0-NLMS algorithm

Sheng Zhang, Wei Xing Zheng

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

![CDATA[For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NLMS) can offer improved convergence performance than the standard normalized least mean square (NLMS) algorithm. However, in the l0-NLMS algorithm, both choices of the step-size and zero-attractor parameters involve the conflicting requirement of fast convergence rate and low steady-state error. In this paper, we propose the joint adaptive step-size and zero-attractor l0-NLMS (JASZ-lo-NLMS) algorithm to address this issue. The proposed algorithm can simultaneously estimate the optimal step-size and zero-attractor derived by minimizing the mean-square deviation (MSD) at each iteration. Simulations are conducted to demonstrate the efficiency of the proposed algorithm in different signal-to-noise ratio (SNR) and sparse channel environments.]]

Original language	English
Title of host publication	2018 IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings, 27-30 May 2018, Florence, Italy
Publisher	IEEE
Number of pages	4
ISBN (Print)	9781538648810
DOIs	https://doi.org/10.1109/ISCAS.2018.8351489
Publication status	Published - 2018
Event	IEEE International Symposium on Circuits and Systems - Duration: 27 May 2018 → …

Publication series

Name
ISSN (Print)	2379-447X

Conference

Conference	IEEE International Symposium on Circuits and Systems
Period	27/05/18 → …

Keywords

adaptive filters
signal processing

Access to Document

10.1109/ISCAS.2018.8351489

Cite this

@inproceedings{5c36ce457ee2480c9adb41c9013b9169,

title = "Joint adaptive step-size and zero-attractor parameters for l0-NLMS algorithm",

abstract = "![CDATA[For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NLMS) can offer improved convergence performance than the standard normalized least mean square (NLMS) algorithm. However, in the l0-NLMS algorithm, both choices of the step-size and zero-attractor parameters involve the conflicting requirement of fast convergence rate and low steady-state error. In this paper, we propose the joint adaptive step-size and zero-attractor l0-NLMS (JASZ-lo-NLMS) algorithm to address this issue. The proposed algorithm can simultaneously estimate the optimal step-size and zero-attractor derived by minimizing the mean-square deviation (MSD) at each iteration. Simulations are conducted to demonstrate the efficiency of the proposed algorithm in different signal-to-noise ratio (SNR) and sparse channel environments.]]",

keywords = "adaptive filters, signal processing",

author = "Sheng Zhang and Zheng, {Wei Xing}",

year = "2018",

doi = "10.1109/ISCAS.2018.8351489",

language = "English",

isbn = "9781538648810",

publisher = "IEEE",

booktitle = "2018 IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings, 27-30 May 2018, Florence, Italy",

note = "IEEE International Symposium on Circuits and Systems ; Conference date: 27-05-2018",

}

TY - GEN

T1 - Joint adaptive step-size and zero-attractor parameters for l0-NLMS algorithm

AU - Zhang, Sheng

AU - Zheng, Wei Xing

PY - 2018

Y1 - 2018

N2 - ![CDATA[For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NLMS) can offer improved convergence performance than the standard normalized least mean square (NLMS) algorithm. However, in the l0-NLMS algorithm, both choices of the step-size and zero-attractor parameters involve the conflicting requirement of fast convergence rate and low steady-state error. In this paper, we propose the joint adaptive step-size and zero-attractor l0-NLMS (JASZ-lo-NLMS) algorithm to address this issue. The proposed algorithm can simultaneously estimate the optimal step-size and zero-attractor derived by minimizing the mean-square deviation (MSD) at each iteration. Simulations are conducted to demonstrate the efficiency of the proposed algorithm in different signal-to-noise ratio (SNR) and sparse channel environments.]]

AB - ![CDATA[For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NLMS) can offer improved convergence performance than the standard normalized least mean square (NLMS) algorithm. However, in the l0-NLMS algorithm, both choices of the step-size and zero-attractor parameters involve the conflicting requirement of fast convergence rate and low steady-state error. In this paper, we propose the joint adaptive step-size and zero-attractor l0-NLMS (JASZ-lo-NLMS) algorithm to address this issue. The proposed algorithm can simultaneously estimate the optimal step-size and zero-attractor derived by minimizing the mean-square deviation (MSD) at each iteration. Simulations are conducted to demonstrate the efficiency of the proposed algorithm in different signal-to-noise ratio (SNR) and sparse channel environments.]]

KW - adaptive filters

KW - signal processing

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:46673

U2 - 10.1109/ISCAS.2018.8351489

DO - 10.1109/ISCAS.2018.8351489

M3 - Conference Paper

SN - 9781538648810

BT - 2018 IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings, 27-30 May 2018, Florence, Italy

PB - IEEE

T2 - IEEE International Symposium on Circuits and Systems

Y2 - 27 May 2018

ER -

Joint adaptive step-size and zero-attractor parameters for l0-NLMS algorithm

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