Network under Control: Multi-Vehicle E2E Measurements for AI-based QoS Prediction
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today's vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.
Details
Originalsprache | Englisch |
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Titel | 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021 |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 1432-1438 |
Seitenumfang | 7 |
ISBN (elektronisch) | 9781728175867 |
Publikationsstatus | Veröffentlicht - 13 Sept. 2021 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC |
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Band | 2021-September |
Konferenz
Titel | 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021 |
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Dauer | 13 - 16 September 2021 |
Stadt | Virtual, Helsinki |
Land | Finnland |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Artificial Intelligence, E2E Measurements, Machine Learning, Network Dynamics, Quality of Service Prediction