An incremental noise constrained LMS algorithm

While a lot of research has focused on diffusion-based algorithms for distributed estimation over adaptive networks, incremental algorithms have not been studied to that extent. Here we present an incremental scheme-based distributed least mean square algorithm that uses the variance of the additive...

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Bibliographic Details
Published inSignal processing Vol. 213; p. 109187
Main Authors Saeed, Muhammad Omer Bin, Zerguine, Azzedine, Hameed, Usman, Khawaja, Sajid Gul, Hammi, Oualid
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2023
Subjects
Distributed estimation
Incremental LMS algorithm
Noise constrained LMS algorithm
Noise constrained LMS algorithm
Incremental LMS algorithm
Distributed estimation
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ISSN0165-1684
1872-7557
DOI10.1016/j.sigpro.2023.109187

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Summary:While a lot of research has focused on diffusion-based algorithms for distributed estimation over adaptive networks, incremental algorithms have not been studied to that extent. Here we present an incremental scheme-based distributed least mean square algorithm that uses the variance of the additive noise as a constraint. The proposed algorithm is derived and then its theoretical performance analysis is presented. Simulation results for different scenarios are then presented and it is shown that the simulation results corroborate very well the theoretical findings. More importantly, our algorithm outperforms, a recently proposed algorithm, the variable step-size incremental-based LMS (VSSILMS) algorithm.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2023.109187