Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149308" target="_blank" >RIV/00216305:26220/23:PU149308 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/10268872" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10268872</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MCOM.004.2200723" target="_blank" >10.1109/MCOM.004.2200723</a>
Alternative languages
Result language
angličtina
Original language name
Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE COMMUNICATIONS MAGAZINE
ISSN
0163-6804
e-ISSN
1558-1896
Volume of the periodical
61
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
106-112
UT code for WoS article
001080991100009
EID of the result in the Scopus database
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