Diffusion LMS algorithms with multi combination for distributed estimation: Formulation and performance analysis
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SCOPUS
- Title
- Diffusion LMS algorithms with multi combination for distributed estimation: Formulation and performance analysis
- Authors
- Kong, Jun-Taek; SEUNGJUN, SHIN; Lee, Jae-Woo; Kim, Seong-Eun; Song, Woo-Jin
- Date Issued
- 2017-12
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Abstract
- We propose diffusion least-mean-square (LMS) algorithms that use multi-combination step. We allow each node in the network to use information from multi-hop neighbors to approximate a global cost function accurately. By minimizing this cost and dividing multi-hop range summation into 1-hop range combination steps, we derive new diffusion LMS algorithms. The resulting distributed algorithms consist of adaptation and multi-combination step. Multi combination allows each node to use information from non-adjacent nodes at each time instant, thereby reducing steady-state error. We analyzed the output to derive stability conditions and to quantify the transient and steady-state behaviors. Theoretical and experimental results indicate that the proposed algorithms have lower steady-state error compared to the conventional diffusion LMS algorithms. We also propose a new combination rule for the multi combination step which can further improve the estimation performance of the proposed algorithms. (C) 2017 Elsevier Inc. All rights reserved.
- Keywords
- VARIABLE STEP-SIZE; LEAST-MEAN SQUARES; COGNITIVE RADIO NETWORKS; ADAPTIVE NETWORKS; CONSENSUS ALGORITHMS; CONVEX-OPTIMIZATION; SUBGRADIENT METHODS; TRANSIENT ANALYSIS; ECHO CANCELLATION; LEARNING-BEHAVIOR
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/50457
- DOI
- 10.1016/j.dsp.2017.09.004
- ISSN
- 1051-2004
- Article Type
- Article
- Citation
- DIGITAL SIGNAL PROCESSING, vol. 71, page. 117 - 130, 2017-12
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