University of Oulu

H. Shiri, M. A. Tinati, M. Codreanu and G. Azarnia, "Distributed sparse diffusion estimation with reduced communication cost," in IET Signal Processing, vol. 12, no. 8, pp. 1043-1052, 10 2018, https://doi.org/10.1049/iet-spr.2017.0377

Distributed sparse diffusion estimation with reduced communication cost

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Author: Shiri, Hamid1; Tinati, Mohammad Ali1; Codreanu, Marian2;
Organizations: 1Department of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran
2Center for Wireless Communications, University of Oulu, Oulu 90014, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020040810759
Language: English
Published: Institution of Electrical Engineers, 2018
Publish Date: 2020-04-08
Description:

Abstract

The issue considered in the current study is the problem of adaptive distributed estimation based on diffusion strategy which can exploit sparsity in improving estimation error and reducing communications. It has been shown that distributed estimation leads to a good performance in terms of the error value, convergence rate, and robustness against node and link failures in wireless sensor networks. However, the main focus of many works in the field of distributed estimation research is on convergence speed and estimation error, neglecting the fact that communications among the nodes require a lot of transmissions. In this work, the focus is on a solution based on sparse diffusion least mean squares (LMS) algorithm, and a new version of sparse diffusion LMS algorithm is proposed which takes both communications and error cost into account. Also, the computation complexity and communication cost for every node of the network, as well as performance analysis of the proposed strategy, is provided. The performance of the proposed method in comparison with the existing methods is illustrated by means of simulations in terms of computational and communicational cost, and flexibility to signal changes.

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Series: IET signal processing
ISSN: 1751-9675
ISSN-E: 1751-9683
ISSN-L: 1751-9675
Volume: 12
Issue: 8
Pages: 1043 - 1052
DOI: 10.1049/iet-spr.2017.0377
OADOI: https://oadoi.org/10.1049/iet-spr.2017.0377
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © The Institution of Engineering and Technology 2018. This is the author's accepted manuscript version. Definitive Version of Record can be found online at: https://doi.org/10.1049/iet-spr.2017.0377.