Modeling and Mitigating Errors in Belief Propagation for Distributed Detection
Abdi, Y., & Ristaniemi, T. (2021). Modeling and Mitigating Errors in Belief Propagation for Distributed Detection. IEEE Transactions on Communications, 69(5), 3286-3297. https://doi.org/10.1109/TCOMM.2021.3056679
Published inIEEE Transactions on Communications
© Authors, 2021
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 multidimensional hypothesis test over binary random variables. 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. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
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