AI & ML
B-ExposNet: Bayesian Deep Learning for EMF Exposure Mapping with Uncertainty Quantification
arXiv eess.SP0 views
arXiv:2610.41913v1 Announce Type: new 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.
Source：https://arxiv.org/abs/2610.41913PreviousImproving Spectral Resolution via Simultaneous Extracochlear and Apical Ground Contacts: A Neural Simulation StudyNextGrid-Refinement SBL for AFDM Channel Estimation Under Fractional Delay and Fractional Doppler Channels
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