Title :

EME-Net: A U-net-based Indoor EMF Exposure Map Reconstruction Method

Author(s) :

Mallik, Mohammed [Auteur correspondant]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Kharbech, Sofiane [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Mazloum, Taghrid [Auteur]
Laboratoire Traitement et Communication de l'Information [LTCI]
Wang, Shanshan [Auteur]
Laboratoire Traitement et Communication de l'Information [LTCI]
Wiart, Joe [Auteur]
Radio-Fréquences Microondes et Ondes Millimétriques [RFM2]
Gaillot, Davy [Auteur]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Clavier, Laurent [Auteur]
Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]

Conference title :

2022 16th European Conference on Antennas and Propagation (EuCAP)

City :

Madrid

Country :

Espagne

Start date of the conference :

2022-03-27

Book title :

2022 16th European Conference on Antennas and Propagation (EuCAP)

Publication date :

2022-05-11

English keyword(s) :

EMF exposure
optimization
convolutional neural network
image reconstruction

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

In wireless communication systems, in order to respond to the perception of risks related to electromagnetic field exposure and allocate radio resources, the estimation of the received power and exposure map is an essential ...
Show more >In wireless communication systems, in order to respond to the perception of risks related to electromagnetic field exposure and allocate radio resources, the estimation of the received power and exposure map is an essential task and a challenge. This paper proposes an algorithm for estimating electromagnetic field exposure maps using U-net architecture based on convolutional neural networks. The power map estimation is transformed into an image reconstruction task by image color mapping, where every pixel value of the image represents received power intensity. The designed model learns wireless signal propagation characteristics in a realistic indoor environment while considering various positions of the Wi-Fi access points. Results show that indoor propagation phenomena and environment models can be learned from data producing an accurate power map to measure the electromagnetic field.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL