Session 4: VAP-1 – 5G based localization services and digital twin
Wednesday, 8 June 2022, 10:30-12:00
Session Chair: TBD ( , )
Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection
Marcus Henninger (University of Stuttgart & Nokia Bell Labs, Germany); Traian E Abrudan (Nokia Bell Labs, Finland); Silvio Mandelli (Nokia Bell Labs, Germany); Maximilian Arnold (Nokia Stuttgart, Germany); Stephan Saur (Nokia Bell Labs, Germany); Veli-Matti Kolmonen (Nokia Bell Labs, Finland); Siegfried Klein and Thomas Schlitter (Nokia Bell Labs, Germany); Stephan ten Brink (University of Stuttgart, Germany)
The continuously increasing bandwidth and antenna aperture available in wireless networks laid the foundation for developing competitive positioning solutions relying on communications standards and hardware. However, poor propagation conditions such as non-line of sight (NLOS) and rich multipath still pose many challenges due to outlier measurements that significantly degrade the positioning performance.
In this work, we introduce an iterative positioning method that reweights the time of arrival (ToA) and angle of arrival (AoA) measurements originating from multiple locators in order to efficiently remove outliers. In contrast to existing approaches that typically rely on a single locator to set the time reference for the time difference of arrival (TDoA) measurements corresponding to the remaining locators, and whose measurements may be unreliable, the proposed iterative approach does not rely on a reference locator only. The resulting robust position estimate is then used to initialize a computationally efficient gradient search to perform maximum likelihood position estimation.
Our proposal is validated with an experimental setup at 3.75 GHz with 5G numerology in an indoor factory scenario, achieving an error of less than 50 cm in 95% of the measurements. To the best of our knowledge, this paper describes the first proof of concept for 5G-based joint ToA and AoA localization.
Uncertainty Quantification of 5G Positioning as a Location Data Analytics Function
Stefania Bartoletti (National Research Council of Italy (IEIIT-CNR), Italy); Giacomo Bernini (Nextworks, Italy); Ivan Palamà (University of Rome Tor Vergata & CNIT, Italy); Michael De Angelis (Nextworks, Italy); Lorenzo Maria Monteforte (University of Rome Tor Vergata, Italy); Takai Eddine Kennouche (VIAVI Solutions, France); Konstantinos Tsagkaris (Incelligent, Greece); Giuseppe Bianchi and Nicola Blefari-Melazzi (University of Rome “Tor Vergata”, Italy)
Mobile positioning is a fundamental service of 5G as it enables a number of applications that rely on location information and location-based analytics. In many applications, it is important to quantify the uncertainty associated with position estimation, for example, for confidence assessment on the location data and anomaly detection, as well as for location data fusion from heterogenous technologies. In this paper, we propose uncertainty quantification as a location data analytics function. First, we introduce an indicator of positioning uncertainty based on the residual measurement error, which does not require the ground truth knowledge. Then, we train and update an uncertainty map of a monitored environment by leveraging the position estimates and location-based measurements collected by multiple users. Such uncertainty map can be used to predict the positioning uncertainty level in any point of a monitored environment. Finally, we propose an implementation of such functionality as an analytics function within the 5G architecture. The functionality is then deployed in a virtualized environment and, using system-level simulations under different propagation conditions, we show how the uncertainty predicted through the proposed method is highly correlated with the true positioning error.
Prediction of TOA-Based Localization Accuracy Using CRLB and 3D Buildings with Field Trial Validation
Christophe Villien (CEA, France); Nicolas Deparis (CEA-Leti, France); Valérian Mannoni (CEA, France); Sébastien de Rivaz (CEA-LETI, France)
In Low Power Wide Area (LPWA) networks, radio localization based on Time of Arrival (TOA) measurements collected from gateways synchronized on GPS time is an appealing technology for Internet of Things (IoT). However, it is hard to predict the actual accuracy that could be expected from a given deployment without taking into account the propagation channel complexity. This paper proposes a new approach to generate a localization accuracy map (LAM) based on Cram´er-Rao lower bound (CRLB) and 3D buildings models to predict the propagation conditions. A comparison with measurements from LPWA field trials conducted in the city of Grenoble (France) is presented, comprising more than 300 000 LPWA transmissions collected. We show that for an infrastructure composed of 6 LPWA gateways, our LAM is able to predict the performance obtained with an maximum likelihood estimator (MLE) localization algorithm with only 12% of mismatch.
Demonstration and Evaluation of Precise Positioning for Connected and Automated Mobility Services
Julia Igual, Marisa Catalan and Miguel Catalan-Cid (i2CAT Foundation, Spain); Francisco Vazquez Gallego (CTTC, Spain); Javier Fernandez Hidalgo (I2CAT, Spain); Raul Muñoz (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain); Roshan Sedar (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain); Ramon Casellas and Ricard Vilalta (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain); Anna Calveras (Universidad Politecnica de Catalunya (UPC), Spain); Josep Paradells (Universitat Politecnica de Catalunya, Spain); Mathieu Lefebvre, Frédéric Gardes and Jean Marc Odinot (Orange, France); Francesca Moscatelli and Giada Landi (Nextworks, Italy); Soumya Kanti Datta and Jérôme Härri (EURECOM, France); Rodrigo Silva (PSA, France); Xavi Vilajosana (Worldsensing, Spain)
Cooperative, Connected and Automated Mobility (CCAM) services require precise and reliable localization services able to infer and track the position of a vehicle with lane accuracy. The H2020 5GCroCo project, which trials 5G technologies in the European cross-border corridor along France, Germany and Luxembourg, as well as in five small-scale trial sites, considers different technologies to enhance vehicle localization, including GPS-Real Time Kinematic (GPS-RTK), Ultra-WideBand (UWB) and Inertial Sensors (INS). This paper presents a compact prototype, which integrates these localization technologies with 5GCroCo’s On-Board Unit (OBU) equipment, and its evaluation within the scope of the Anticipated Cooperative Collision Avoidance (ACCA) Use Case demonstrated in Barcelona small-scale trial site.
Simulation-Based Digital Twin for 5G Connected Automated and Autonomous Vehicles
Martina Barbi (Instituto de Telecomunicaciones y Aplicaciones Multimedia (iTEAM), Spain); Alejandro Anton and Arturo Mrozowski Handzel (Universitat Politècnica de València, Spain); Saúl Inca (iTEAM Research Institute, Universitat Politècnica de València, Spain); David Garcia-Roger and Jose F Monserrat (Universitat Politècnica de València, Spain)
Cooperative, connected and automated mobility (CCAM) across Europe requires efficient and coordinated solutions to overcome cross-borders service discontinuity. The provision of CCAM services across different countries has enormous innovative business potential. However, ensuring uninterrupted connectivity poses technical challenges that 5G technologies aim to solve. In the mark of the 5G-CARMEN project, good progress has been made to develop wireless infrastructure technologies for 5G deployment at the cross-borders. However, besides the field trials, testing the performance and limits of connected vehicles technology to enable safer real-world deployment is a real necessity, particularly, when considering fully autonomous driving vehicles. Digital Twin represents an innovative solution that provides a software replica of the 5G physical network allowing the study and optimization of real-world use cases. This paper presents a simulation-based digital twin developed to emulate connected automated and autonomous vehicles performing cooperative lane change/merge maneuvers, as defined in 5G-CARMEN, in a cross-border scenario.