2026-09-19T04:55:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/576902022-01-13T07:54:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Moe Win.
Ferner, Ulric John
Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2010-08-30T14:38:29Z
2010-08-30T14:38:29Z
2010
2010
http://hdl.handle.net/1721.1/57690
639233852
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.
80 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Distributed belief propagation and its generalizations for location-aware networks
Thesis
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<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.</Abstract>
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