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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Bibliographic Details
Published in:IET signal processing Vol. 10; no. 1; pp. 37 - 45
Main Authors: Huang, Wei, Yang, Xi, Liu, Duanyang, Chen, Shengyong
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.02.2016
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2015.0033