Network calibration method based on a Bayesian approach using frequency modulation communications for wireless systems over additive white Gaussian noise channel

Abstract

Wireless sensor networks offer flexibility to monitor environmental conditions. However, in some hostile environments with changing propagation occurrences, it is challenging to guarantee a network free from systematic errors (biases), those errors can corrupt the data gathered from the network. The authors intend to address this problem using a Bayesian approach. This study presents a new Bayesian wireless network model based on a belief propagation algorithm over a noisy communication, to seek the calibration of the network for correlated sources. They set up a point-to-point communication model, consisting of a frequency modulation signal at the transmitter and a discrete-time phase-locked loop structure at the receiver over an additive white Gaussian noise channel. Then, they use a Bayesian approach to model the propagation of information, and analyse the noise impact on their system. The simulation results show significant improvement within a few time instants in the mean squared error of the sensors internal states. An evaluation in a noisy environment confirms the robustness of the proposed system.