Modeling and Mitigating Errors in Belief Propagation for Distributed Detection
Abstract
We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed binary hypothesis test where the joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distributed optimization framework to enhance the system performance by mitigating the impact of errors via a distributed linear data-fusion scheme. Finally, we compare the results of the proposed analysis with the existing works and visualize, via computer simulations, the performance gain obtained by the proposed optimization.
Cite
@article{arxiv.2004.05220,
title = {Modeling and Mitigating Errors in Belief Propagation for Distributed Detection},
author = {Younes Abdi and Tapani Ristaniemi},
journal= {arXiv preprint arXiv:2004.05220},
year = {2020}
}