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            <funder>The French government’s Beyond5G initiative, which was sponsored as a component of the country’s future investment program and strategy for economic recovery, provided partial funding for this project. The COST action CA20120 INTERACT is another funding source for this project. This work has also been partially funded by Grants PID2020-112675RB-C41 and PID2019-107885GB-C33 by MCIN/AEI/10.13039/501100011033.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.</funder>
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                <term xml:lang="en">EMF exposure</term>
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              <p>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.</p>
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