M. Skocaj et al., "Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service," in IEEE Communications Magazine, vol. 61, no. 9, pp. 106-112, September 2023, doi: 10.1109/MCOM.004.2200723
Vehicle-to-Everything (V2X) datasets for Machine Learning-based Predictive Quality of Service
|Author:||Skocaj, Marco1; Di Cicco, Nicola2; Zugno, Tommaso3;|
1University of Bologna & WiLab, CNIT, Italy
2Politecnico di Milano, Italy
3Munich Research Center, Huawei Technologies, Duesseldorf GmbH, Germany
4Brno University of Technology, Czech Republic
5University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231005138864
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-10-05
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.
IEEE communications magazine
|Pages:||106 - 112|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This article is based upon work from COST Action INTERACT, CA20120, supported by COST (European Cooperation in Science and Technology).
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