Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs

Sensors (Basel). 2022 Dec 9;22(24):9643. doi: 10.3390/s22249643.


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.

MeSH terms

  • Electromagnetic Fields*

Grants and funding

This research work is done at IRCICA, UAR CNRS 3380, Lille. Special Thanks to Métropole Européenne de Lille (MEL) for supporting this Ph.D. project.