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SKOCAJ, M. DI CICCO, N. ZUGNO, T. BOBAN, M. BLUMENSTEIN, J. PROKEŠ, A. MIKULÁŠEK, T. VYCHODIL, J. MIKHAYLOV, K. TORNATORE, M. DEGLI ESPOSTI, V.
Original Title
Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service
Type
journal article in Web of Science
Language
English
Original Abstract
We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean.
Keywords
Vehicle-to-infrastructure; Power control; Vehicular ad hoc networks; Quality of service; Machine learning; Prediction algorithms; Particle measurements
Authors
SKOCAJ, M.; DI CICCO, N.; ZUGNO, T.; BOBAN, M.; BLUMENSTEIN, J.; PROKEŠ, A.; MIKULÁŠEK, T.; VYCHODIL, J.; MIKHAYLOV, K.; TORNATORE, M.; DEGLI ESPOSTI, V.
Released
1. 9. 2023
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Location
PISCATAWAY
ISBN
1558-1896
Periodical
IEEE COMMUNICATIONS MAGAZINE
Year of study
61
Number
9
State
United States of America
Pages from
106
Pages to
112
Pages count
7
URL
https://ieeexplore.ieee.org/abstract/document/10268872
BibTex
@article{BUT185064, author="Marco {Skocaj} and Nicola {Di Cicco} and Tommaso {Zugno} and Mate {Boban} and Jiří {Blumenstein} and Aleš {Prokeš} and Tomáš {Mikulášek} and Josef {Vychodil} and Konstantin {Mikhaylov} and Massimo {Tornatore} and Vittorio {Degli Esposti}", title="Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service", journal="IEEE COMMUNICATIONS MAGAZINE", year="2023", volume="61", number="9", pages="106--112", doi="10.1109/MCOM.004.2200723", issn="1558-1896", url="https://ieeexplore.ieee.org/abstract/document/10268872" }