Unsupervised algorithms for distributed estimation over adaptive networks

M. O. Bin Saeed*, A. Zerguine, S. A. Zummo, A. H. Sayed

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This work shows how to develop distributed versions of block blind estimation techniques that have been proposed before for batch processing. Using diffusion adaptation techniques, data are accumulated at the nodes to form estimates of the auto-correlation matrices and to carry out local SVD and/or Cholesky decomposition steps. Local estimates at neighborhoods are then aggregated to provide online streaming estimates of the parameters of interest. Simulation results illustrate the performance of the algorithms.

Original languageEnglish
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages1780-1783
Number of pages4
DOIs
StatePublished - 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Keywords

  • Blind estimation
  • Cholesky factorization
  • SVD
  • auto-correlation matrix
  • diffusion strategy

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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