12196
18
15832
5
222437
document
15832
INVE_MEM_2011_94448.pdf
application/pdf
138147
20140922 10:53:41
https://oa.upm.es/12196/1/INVE_MEM_2011_94448.pdf
12196
1
application/pdf
application/pdf
en
public
INVE_MEM_2011_94448.pdf
archive
1903
disk0/00/01/21/96
20120830 10:14:52
20160421 11:24:09
20120830 10:14:52
conference_item
show
0

Penna
Federico

Wymeersch
Henk

Savic
Vladimir
Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing
pub
 telecomunicaciones
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.
2011
published
IEEE
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5967807
public
paper
EEUU
20011 IEEE of Statistical Signal Processing Workshop (SSP)
Niza, Francia
28/06/2011  30/06/2011
conference
Telecomunicacion
Senales
TRUE
9781457705694
Proceedings of 20011 IEEE of Statistical Signal Processing Workshop (SSP)
byncnd