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research article

Performance Limits for Distributed Estimation Over LMS Adaptive Networks

Zhao, Xiaochuan
•
Sayed, Ali H.  
2012
IEEE Transactions on Signal Processing

In this work, we analyze the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks. The results highlight some useful properties for distributed adaptation in comparison to fusion-based centralized solutions. The analysis establishes that, by optimizing over the combination weights, diffusion strategies can deliver lower excess-mean-square-error than centralized solutions employing traditional block or incremental LMS strategies. We first study in some detail the situation involving combinations of two adaptive agents and then extend the results to generic N -node ad-hoc networks. In the latter case, we establish that, for sufficiently small step-sizes, diffusion strategies can outperform centralized block or incremental LMS strategies by optimizing over left-stochastic combination weighting matrices. The results suggest more efficient ways for organizing and processing data at fusion centers, and present useful adaptive strategies that are able to enhance performance when implemented in a distributed manner.

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Type
research article
DOI
10.1109/TSP.2012.2204985
Author(s)
Zhao, Xiaochuan
•
Sayed, Ali H.  
Date Issued

2012

Publisher

IEEE

Published in
IEEE Transactions on Signal Processing
Volume

60

Issue

10

Start page

5107

End page

5124

Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
ASL  
Available on Infoscience
December 19, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/143285
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