http://iet.metastore.ingenta.com
1887

Component-wise variable step-size diffusion least mean square algorithm for distributed estimation

Component-wise variable step-size diffusion least mean square algorithm for distributed estimation

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors propose a novel component-wise variable step-size diffusion least mean square (CWVSS-DLMS) algorithm for distributed estimation. Different from the traditional variable step-size DLMS (VSS-DLMS) algorithms in which the updating of all components in the weight vector are the same, the step sizes vary from each other on all components at each iteration in the CWVSS-DLMS algorithm. After deriving the CWVSS-DLMS algorithm, they perform theoretical analysis in terms of mean stability and mean-square behaviour. They have also compared the performance of the CWVSS-DLMS algorithm with several other DLMS algorithms through numerical simulations in both stationary and non-stationary environments. Simulation results show that the performance of the CWVSS-DLMS algorithm is more outstanding than the fixed step-size DLMS algorithm, several non-component-wise VSS-DLMS algorithms and existing component-wise VSS-DLMS algorithms in balancing high convergence rates and low steady-state misadjustment. Moreover, they have investigated the performance of the CWVSS-DLMS algorithm for estimating sparse parameter in a distributed way. Simulation results show that the CWVSS-DLMS algorithm can yield satisfying performance in sparsely distributed estimation regardless of the degree of sparsity in the real parameter.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5288
Loading

Related content

content/journals/10.1049/iet-spr.2018.5288
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address