Quickest detection of Gauss-Markov random fields

Javad Heydari, Ali Tajer, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

The problem of quickest data-adaptive and sequential search for clusters in a Gauss-Markov random field is considered. In the existing literature, such search for clusters is often performed using fixed sample size and non-adaptive strategies. In order to accommodate large networks, in which data adaptivity leads to significant gains in detection quality and agility, in this paper sequential and data-adaptive detection strategies are proposed and are shown to enjoy asymptotic optimality. The quickest detection problem is abstracted by adopting an acyclic dependency graph to model the mutual effects of different random variables in the field and decision making rules are derived for general random fields and specialized for Gauss-Markov random fields. Performance evaluations demonstrate the gains of the data-adaptive schemes over existing techniques in terms of sampling complexity and error exponents.

Original languageEnglish (US)
Title of host publication2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages808-814
Number of pages7
ISBN (Electronic)9781509018239
DOIs
StatePublished - Apr 4 2016
Event53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 - Monticello, United States
Duration: Sep 29 2015Oct 2 2015

Publication series

Name2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015

Other

Other53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
CountryUnited States
CityMonticello
Period9/29/1510/2/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Systems Engineering

Keywords

  • Gauss-Markov random field
  • Quickest detection
  • selection policy
  • sequential sampling

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