Diffusion LMS with component-wise variable step-size over sensor networks

In this study, the authors propose a novel component-wise variable step-size (CVSS) diffusion distributed algorithm for estimating a specific parameter over sensor networks. The novelty of the CVSS algorithm is that step-sizes vary from each other on different components at each iteration. They deri...

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Vydáno v:	IET signal processing Ročník 10; číslo 1; s. 37 - 45
Hlavní autoři:	Huang, Wei, Yang, Xi, Liu, Duanyang, Chen, Shengyong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.02.2016
Témata:	Algorithms component wise variable step size Convergence convergence of numerical methods convergence rate CVSS diffusion distributed algorithm Deviation Diffusion diffusion LMS algorithm distributed algorithms global MSD Least mean squares least mean squares methods Mathematical models mean square deviation Networks parameter estimation relative estimation error relative MSD RMSD sensor network Sensors steady‐state value wireless sensor networks least mean squares methods wireless sensor networks convergence of numerical methods RMSD relative estimation error component wise variable step size sensor network distributed algorithms diffusion LMS algorithm steady-state value parameter estimation global MSD relative MSD convergence rate mean square deviation CVSS diffusion distributed algorithm
ISSN:	1751-9675, 1751-9683, 1751-9683
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Shrnutí:	In this study, the authors propose a novel component-wise variable step-size (CVSS) diffusion distributed algorithm for estimating a specific parameter over sensor networks. The novelty of the CVSS algorithm is that step-sizes vary from each other on different components at each iteration. They derive the steady-state value of global mean-square deviation (MSD) and relative MSD (RMSD). In the numerical simulations, they compare the proposed CVSS algorithm with several other least mean square (LMS) algorithms. Results show that, when compared with these other algorithms, the CVSS algorithm can effectively reduce steady-state value and speed up convergence rate of RMSD while not sacrificing the convergence rate of MSD. Results also reveal that the proposed CVSS algorithm can achieve reduced difference of steady-state values of relative estimation error on various components.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1751-9675 1751-9683 1751-9683
DOI:	10.1049/iet-spr.2015.0033