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arXiv · 2610.41913

B-ExposNet: Bayesian Deep Learning for EMF Exposure Mapping with Uncertainty Quantification

Yukun Liu·Shanshan Wang·Paul Lagouanelle·Yarui Zhang·Joe Wiart
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Abstract

Electromagnetic field exposure in urban environments is highly variable, and conventional exposure maps provide a deterministic estimate, without considering its uncertainty. To address the gap, this paper proposes B-ExposNet, a Bayesian neural network for spatial exposure estimation with uncertainty quantification. B-ExposNet extends ExposNet by treating the weights of regression head as random variables, inferred by variational inference, so that the model provides probabilistic estimates from posterior predictive distribution. The method is evaluated on drive-test measurement campaigns conducted in three French cities, and compared with conventional method such as universal Kriging, in both predictive accuracy and the quality of the estimated uncertainty. On the test region, B-ExposNet achieves a mean absolute percentage error of 28.19\% and a continuous ranked probability score of 0.198~V/m, improving on canonical methods in both accuracy and uncertainty quantification.

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Yukun Liu, Shanshan Wang, Paul Lagouanelle, Yarui Zhang, Joe Wiart. 2026-10-30. B-ExposNet: Bayesian Deep Learning for EMF Exposure Mapping with Uncertainty Quantification. https://arxiv.org/abs/2610.41913

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