Descripción
|
|
---|---|
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. | |
Internacional
|
Si |
Nombre congreso
|
IEEE Statistical Signal Processing Workshop (SSP) |
Tipo de participación
|
960 |
Lugar del congreso
|
Nice, France |
Revisores
|
Si |
ISBN o ISSN
|
978-1-4577-0569-4 |
DOI
|
10.1109/SSP.2011.5967807 |
Fecha inicio congreso
|
28/06/2011 |
Fecha fin congreso
|
30/06/2011 |
Desde la página
|
733 |
Hasta la página
|
736 |
Título de las actas
|
IEEE Proc. of Statistical Signal Processing |