Fine Tuning an AI-based Indoor Radio Propagation Model with Crowd-sourced Data
In: European Conference on Antennas and Propagation ; https://hal.science/hal-04528361 ; European Conference on Antennas and Propagation, Mar 2024, Glasgow, United Kingdom, 2024
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Zugriff:
International audience ; Recent years have known the development of radio propagation models (RPM) and specially the AI-based ones. These models are interesting for many applications such as radio planing and design, fingerprinting-based localization, radio resources management etc. However most of the proposed AIbased RPMs have been trained on simulated radio data making them not ready or reliable for real condition applications. Inthis work we tackle the problem of learning and fine-tuning an AI-based RPM from simulated data to real world radio measurements. We raise the inherent problems and limitations and then propose solutions to overcome them. The study has been focused on 5 GHz Wi-Fi and Home network environment.
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Fine Tuning an AI-based Indoor Radio Propagation Model with Crowd-sourced Data
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Autor/in / Beteiligte Person: | Cisse, Cheick Tidiani ; Guillet, Valery ; Baala, Oumaya ; Spies, François ; Caminada, Alexandre ; Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST) ; Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC) ; Université Bourgogne Franche-Comté COMUE (UBFC)-Université Bourgogne Franche-Comté COMUE (UBFC) ; Labs, Orange ; Laboratoire d'Informatique Signaux et Systèmes de Sophia-Antipolis (I3S) |
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Zeitschrift: | European Conference on Antennas and Propagation ; https://hal.science/hal-04528361 ; European Conference on Antennas and Propagation, Mar 2024, Glasgow, United Kingdom, 2024 |
Veröffentlichung: | HAL CCSD, 2024 |
Medientyp: | Konferenz |
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