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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE COMMUNICATIONS MAGAZINE

ISSN

0163-6804

e-ISSN

1558-1896

Svazek periodika

61

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

106-112

Kód UT WoS článku

001080991100009

EID výsledku v databázi Scopus

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