International audience

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.

Although LoRa modulation is known for its robustness allowing devices to communicate kilometers away, it suffers from coverage issues especially where density of gateways is low or in dense urban areas. However, a simple 2-hop LoRaWAN communication can seamlessly extend the network coverage and even improve both data extraction rate (DER) and energy consumption. Experiments in this paper figure out cases under non line of sight (NLoS) conditions where relaying performs better. Regarding the exponential increase of airtime with the spreading factor (SF), as soon as a 2-hop SF7 link allows a better DER as a single hop SF8 link, it becomes more attractive to use a relay. Indeed, energy consumption is linked to the airtime - or the amount of time to send a frame - explaining the energy efficiency with the 2-hop relaying protocol. To verify this assert, LoRa network coverage with a testbed in urban environmens is first compared. Then, simulations help to study energy consumption according to the case study. Results prove that relaying effectively gives better results under NLoS conditions, particularly in dense areas, by improving the DER. It also highlights the limits of LoRa in urban areas where the DER can be under 0.5 using SF12 and with less than a kilometer range.