Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs

Mohammed Mallik; Angesom Ataklity Tesfay; Benjamin Allaert; Esteban Egea Lopez; Jose-Maria Molina-Garcia Pardo; Redha Kassi; Joe Wiart; Davy P. Gaillot; Laurent Clavier

doi:10.20944/preprints202211.0040.v1

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preprints.org > engineering > telecommunications > doi: 10.20944/preprints202211.0040.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs

Mohammed Mallik

^* ,

Angesom Ataklity Tesfay

Benjamin Allaert

Esteban Egea Lopez

Jose-Maria Molina-Garcia Pardo

Version 1 : Received: 29 October 2022 / Approved: 2 November 2022 / Online: 2 November 2022 (03:43:14 CET)

A peer-reviewed article of this Preprint also exists.

Mallik, M.; Tesfay, A.A.; Allaert, B.; Kassi, R.; Egea-Lopez, E.; Molina-Garcia-Pardo, J.-M.; Wiart, J.; Gaillot, D.P.; Clavier, L. Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs. Sensors 2022, 22, 9643. Mallik, M.; Tesfay, A.A.; Allaert, B.; Kassi, R.; Egea-Lopez, E.; Molina-Garcia-Pardo, J.-M.; Wiart, J.; Gaillot, D.P.; Clavier, L. Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs. Sensors 2022, 22, 9643.

Abstract

With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional Generative Adversarial Network to address this issue. The main objective is to reconstruct the electromagnetic field exposure map accurately according to the environment’s topology from a few sensors located in an outdoor urban environment. The model is trained to learn and estimate the propagation characteristics of the electromagnetic field according to the topology of a given environment. In addition, the conditional Generative Adversarial Network based electromagnetic field mapping is compared with simple kriging. Results show that the proposed method produces accurate estimates and is a promising solution for exposure map reconstruction.

Keywords

EMF exposure; conditional generative adversarial network; optimization

Subject

Engineering, Telecommunications

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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