2023-03-27T14:49:37Z
https://oa.upm.es/cgi/oai2
oai:oa.upm.es:12196
2016-04-21T11:24:09Z
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747970653D636F6E666572656E63655F6974656D
Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing
Penna, Federico
Wymeersch, Henk
Savic, Vladimir
Telecommunications
Belief propagation (BP) is a technique for distributed inference in wireless networks and is often used even when the underlying graphical model contains cycles. In this paper, we propose a uniformly reweighted BP scheme that reduces the impact of cycles by weighting messages by a constant ?edge appearance probability? rho ? 1. We apply this algorithm to distributed binary hypothesis testing problems (e.g., distributed detection) in wireless networks with Markov random field models. We demonstrate that in the considered setting the proposed method outperforms standard BP, while maintaining similar complexity. We then show that the optimal ? can be approximated as a simple function of the average node degree, and can hence be computed in a distributed fashion through a consensus algorithm.
E.T.S.I. Telecomunicación (UPM)
https://creativecommons.org/licenses/by-nc-nd/3.0/es/
2011
info:eu-repo/semantics/conferenceObject
Presentation at Congress or Conference
Proceedings of 20011 IEEE of Statistical Signal Processing Workshop (SSP) | 20011 IEEE of Statistical Signal Processing Workshop (SSP) | 28/06/2011 - 30/06/2011 | Niza, Francia
PeerReviewed
application/pdf
eng
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5967807
info:eu-repo/semantics/openAccess
https://oa.upm.es/12196/