2018年9月10日
An Adaptive Combination Rule for Diffusion LMS Based on Consensus Propagation
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
- ,
- 巻
- 2018-April
- 号
- 開始ページ
- 3839
- 終了ページ
- 3843
- 記述言語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICASSP.2018.8462277
- 出版者・発行元
- IEEE
Diffusion least-mean-square (LMS) algorithm is a method that estimates an unknown global vector from its linear measurements obtained at multiple nodes in a network in a distributed manner. This paper proposes a novel combination rule in the algorithm used to integrate the local estimates at each node by using the idea of consensus propagation, which is known to be a fast algorithm to achieve the average consensus. Moreover, we optimize constants involved in the proposed combination rule in terms of the steady state mean-square-deviation (MSD) and show an adaptive combination rule, along with an adaptive implementation. Simulation results demonstrate that the proposed combination scheme achieves better MSD performance than conventional combination schemes.
- リンク情報
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- DOI
- https://doi.org/10.1109/ICASSP.2018.8462277
- DBLP
- https://dblp.uni-trier.de/rec/conf/icassp/NakaiH18
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85054208552&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85054208552&origin=inward
- URL
- https://dblp.uni-trier.de/conf/icassp/2018
- URL
- https://dblp.uni-trier.de/db/conf/icassp/icassp2018.html#NakaiH18
- ID情報
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- DOI : 10.1109/ICASSP.2018.8462277
- ISSN : 1520-6149
- DBLP ID : conf/icassp/NakaiH18
- SCOPUS ID : 85054208552