A variable step-size incremental LMS solution for low SNR applications

Muhammad Omer Bin Saeed; Syed Ahmed Pasha; Azzedine Zerguine

doi:10.1016/j.sigpro.2020.107730

A variable step-size incremental LMS solution for low SNR applications

Muhammad Omer Bin Saeed^*, Syed Ahmed Pasha, Azzedine Zerguine

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy.

Original language	English
Article number	107730
Journal	Signal Processing
Volume	178
DOIs	https://doi.org/10.1016/j.sigpro.2020.107730
State	Published - Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

Incremental scheme
Least-mean-square algorithm
Low SNR
Mean-square analysis
Steady-state analysis
Variable step-size

ASJC Scopus subject areas

Control and Systems Engineering
Software
Signal Processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

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10.1016/j.sigpro.2020.107730

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@article{55a13495108846528ac173c0a2eb4381,

title = "A variable step-size incremental LMS solution for low SNR applications",

abstract = "Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy.",

keywords = "Incremental scheme, Least-mean-square algorithm, Low SNR, Mean-square analysis, Steady-state analysis, Variable step-size",

author = "{Bin Saeed}, {Muhammad Omer} and Pasha, {Syed Ahmed} and Azzedine Zerguine",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",

year = "2021",

month = jan,

doi = "10.1016/j.sigpro.2020.107730",

language = "English",

volume = "178",

journal = "Signal Processing",

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T1 - A variable step-size incremental LMS solution for low SNR applications

AU - Bin Saeed, Muhammad Omer

AU - Pasha, Syed Ahmed

AU - Zerguine, Azzedine

PY - 2021/1

Y1 - 2021/1

N2 - Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy.

AB - Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy.

KW - Incremental scheme

KW - Least-mean-square algorithm

KW - Low SNR

KW - Mean-square analysis

KW - Steady-state analysis

KW - Variable step-size

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DO - 10.1016/j.sigpro.2020.107730

M3 - Article

AN - SCOPUS:85089735530

SN - 0165-1684

VL - 178

JO - Signal Processing

JF - Signal Processing

M1 - 107730

ER -

A variable step-size incremental LMS solution for low SNR applications

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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