Distributed belief propagation and its generalizations for location-aware networks

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Author(s)
Ferner, Ulric John
Advisor(s)
Moe Win.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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