Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs
Mohammed Mallik, Angesom Ataklity Tesfay, Benjamin Allaert, R. Kassi, Esteban Egea-López, José‐María Molina‐García‐Pardo, Joe Wiart, Davy P. Gaillot, Laurent Clavier
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.