Affine Combination of Diffusion Strategies over Networks

Danqi Jin, Jie Chen, Cédric Richard, Jingdong Chen, Ali H. Sayed

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strategies for enhanced performance. By assigning a combination coefficient to each node, and using an adaptation mechanism to minimize the network error, we obtain a combined diffusion strategy that benefits from the best characteristics of all component strategies simultaneously in terms of excess-mean-square error (EMSE). Analyses of the universality are provided to show the superior performance of affine combination scheme and to characterize its behavior in the mean and mean-square sense. Simulation results are presented to demonstrate the effectiveness of the proposed strategies, as well as the accuracy of theoretical findings.

Original languageEnglish
Article number9005223
Pages (from-to)2087-2104
Number of pages18
JournalIEEE Transactions on Signal Processing
Volume68
DOIs
StatePublished - 2020

Keywords

  • adaptive fusion
  • affine combination
  • diffusion strategy
  • Distributed optimization
  • stochastic performance

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