Detail publikace

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

SKOCAJ, M. DI CICCO, N. ZUGNO, T. BOBAN, M. BLUMENSTEIN, J. PROKEŠ, A. MIKULÁŠEK, T. VYCHODIL, J. MIKHAYLOV, K. TORNATORE, M. DEGLI ESPOSTI, V.

Originální název

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Vehicle-to-infrastructure; Power control; Vehicular ad hoc networks; Quality of service; Machine learning; Prediction algorithms; Particle measurements

Autoři

SKOCAJ, M.; DI CICCO, N.; ZUGNO, T.; BOBAN, M.; BLUMENSTEIN, J.; PROKEŠ, A.; MIKULÁŠEK, T.; VYCHODIL, J.; MIKHAYLOV, K.; TORNATORE, M.; DEGLI ESPOSTI, V.

Vydáno

1. 9. 2023

Nakladatel

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Místo

PISCATAWAY

ISSN

1558-1896

Periodikum

IEEE COMMUNICATIONS MAGAZINE

Ročník

61

Číslo

9

Stát

Spojené státy americké

Strany od

106

Strany do

112

Strany počet

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"
}