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Journal Articles Sensors Year : 2022

Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs

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.
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hal-03919067 , version 1 (02-01-2023)

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Mohammed Mallik, Angesom Ataklity Tesfay, Benjamin Allaert, Rédha Kassi, Esteban Egea-Lopez, et al.. Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs. Sensors, 2022, 22 (24), pp.9643. ⟨10.3390/s22249643⟩. ⟨hal-03919067⟩
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