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Communication Dans Un Congrès Année : 2023

Accurate E-CID Framework for Indoor Positioning in 5G using Path Tracing and Machine Learning

Résumé

Locating at-risk workers in hospitals using legacy private 5G networks is a daunting task that involves solving the problem of indoor localization using commercial off-the-shelf proprietary hardware. Currently, no full-stack schemes or realistic indoor positioning experiments have been conducted using 5G. In this study, we present the first comprehensive 5G framework that combines fingerprinting with the 3GPP Enhanced Cell ID (E-CID) approach. Our methodology consists of a machine-learning model to deduce the user's position by comparing the signal strength received from the User Equipment (UE) with a reference radio power map. This challenging method has four main contributions. First, the 3GPP protocols and functions are extended to provide open, secure, and universal core network-based localization functions. Second, to generate a reference map, the first paradigm of Optical Radio Power Estimation using Light Analysis (ORPELA) is introduced. Real-world experiments prove that it is reproducible and more accurate than state-of-the-art radio-planning software. Third, machine-learning models are designed, trained, and optimized for an ultra-challenging radio context. Finally, an extensive experimental campaign is conducted to demonstrate the expected indoor localization performance of realistic 5G private networks.
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Dates et versions

hal-04184423 , version 1 (21-08-2023)

Identifiants

  • HAL Id : hal-04184423 , version 1

Citer

Antonin Le Floch, Rahim Kacimi, Pierre Druart, Yoann Lefebvre, André-Luc Beylot. Accurate E-CID Framework for Indoor Positioning in 5G using Path Tracing and Machine Learning. 26th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2023), ACM, Oct 2023, Montreal, Canada. à paraître. ⟨hal-04184423⟩
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