Distributed estimation in diffusion networks using affine least-squares combiners

Author(s)

Fernandez-Bes, Jesus ^[1] ; Azpicueta-Ruiz, Luis A. ^[1] ; Arenas-Garcia, Jeronimo ^[1] ; Silva, Magno T. M. ^[2]

Affiliation

^[1] Univ Carlos III Madrid, Dept Signal Theory & Commun, Leganes 28911 - Spain

^[2] Univ Sao Paulo, Escola Politecn, Dept Elect Syst Engn, BR-05508010 Sao Paulo - Brazil

• Document type: Journal article
• Source: DIGITAL SIGNAL PROCESSING; v. 36, p. 1-14, JAN 2015.
• Web of Science Citations: 11

Abstract

We propose a diffusion scheme for adaptive networks, where each node obtains an estimate of a common unknown parameter vector by combining a local estimate with the combined estimates received from neighboring nodes. The combination weights are adapted in order to minimize the mean-square error of the network employing a local least-squares (LS) cost function. This adaptive diffusion network with LS combiners (ADN-LS) is analyzed, deriving expressions for its network mean-square deviation that characterize the convergence and steady-state performance of the algorithm. Experiments carried out in stationary and tracking scenarios show that our proposal outperforms a state-of-art scheme for adapting the weights of diffusion networks (ACW algorithm from {[}10], both during convergence and in tracking situations. Despite its good convergence behavior, our proposal may present a slightly worse steady-state performance in stationary or slowly-changing scenarios with respect to ACW due to the error inherent to the least-squares adaptation with sliding window. Therefore, to take advantage of these different behaviors, we also propose a hybrid scheme based on a convex combination of the ADN-LS and ACW algorithms. (C) 2014 Elsevier Inc. All rights reserved. (AU)

• FAPESP's process: 13/18041-5 - Combination of adaptive algorithms and distributed adaptive filtering applied to acoustics
• Grantee: Magno Teófilo Madeira da Silva
• Support Opportunities: Research Grants - Visiting Researcher Grant - International
• FAPESP's process: 12/24835-1 - Adaptive algorithms, combinations and applications in deconvolution
• Grantee: Magno Teófilo Madeira da Silva
• Support Opportunities: Regular Research Grants