# Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion

## TL;DR

This paper develops a surrogate model using sparse polynomial chaos expansion to efficiently analyze human electromagnetic exposure in indoor WiFi scenarios, incorporating cross-validation techniques for reliable assessment.

## Contribution

It introduces a surrogate modeling approach with sparse PCE and cross-validation for accurate exposure analysis, addressing computational challenges and model validation issues.

## Key findings

- The surrogate model accurately predicts electromagnetic exposure levels.
- Cross-validation improves model reliability and prevents overfitting.
- Sensitivity analysis identifies key input parameters affecting exposure.

## Abstract

Human exposure induced by wireless communication systems increasingly draws the public attention. Here, an indoor down-link scenario is concerned and the exposure level is statistically analyzed. The electromagnetic field (EMF) emitted by a WiFi box is measured and electromagnetic dosimetry features are evaluated from the whole-body specific absorption rate as computed with a Finite-Difference Time-Domain (a.k.a. FDTD) code. Due to computational cost, a statistical analysis is performed based on a surrogate model, which is constructed by means of so-called sparse polynomial chaos expansion (PCE), where the inner cross validation (ICV) is used to select the optimal hyperparameters during the model construction and assess the model performance. However, the ICV error is optimized and the model assessment tends to be overly optimistic with small data sets. The method of cross-model validation is used and outer cross validation is carried out for the model assessment. The effects of the data preprocessing are investigated as well. Based on the surrogate model, the global sensitivity of the exposure to input parameters is analyzed from Sobol' indices.