Distributed belief propagation using sensor networks with correlated observations

Alfonso Cano, Georgios B Giannakis

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

Abstract

A distributed belief propagation protocol is developed to carry inference and decoding tasks using wireless sensor networks with high-dimensional, correlated observations. Statistical dependencies are modeled using factor graphs. The overall a-posteriori probability is factored so that its factor graph representation can be mapped to the actual communication network. Sum-productmessage passing updates over the graphical model can thus be mapped to messages among sensors. As an application scenario, distributed spectrum sensing is considered. Simulated tests show that exploiting the correlation present among sensor observations can considerably improve sensing performance.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2841-2844
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period3/25/123/30/12

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