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Distributed belief propagation and its generalizations for location-aware networks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 77-80). / This thesis investigates the use of generalized belief propagation (GBP) and belief propagation (BP) algorithms for distributed inference. The concept of a network region graph is introduced, along with several approximation structures that can be distributed across a network. In this formulation, clustered region graphs are introduced to create a network "backbone" across which the computation for inference is distributed. This thesis shows that clustered region graphs have good structural properties for GBP algorithms. We propose the use of network region graphs and GBP for location-aware networks. In particular, a method for representing GBP messages non-parametrically is developed. As an special case, we apply BP algorithms to mobile networks without infrastructure, and we propose heuristics to optimize degree of network cooperation. Numerical results show a five times performance increase in terms of outage probability, when compared to conventional algorithms. / by Ulric John Ferner. / S.M.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/57690
Date January 2010
CreatorsFerner, Ulric John
ContributorsMoe Win., Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics., Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
PublisherMassachusetts Institute of Technology
Source SetsM.I.T. Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Format80 p., application/pdf
RightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582

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