Network under Control: Multi-Vehicle E2E Measurements for AI-based QoS Prediction
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
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
Original language | English |
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Title of host publication | 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1432-1438 |
Number of pages | 7 |
ISBN (electronic) | 9781728175867 |
Publication status | Published - 13 Sept 2021 |
Peer-reviewed | Yes |
Publication series
Series | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC |
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Volume | 2021-September |
Conference
Title | 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021 |
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Duration | 13 - 16 September 2021 |
City | Virtual, Helsinki |
Country | Finland |
Keywords
ASJC Scopus subject areas
Keywords
- Artificial Intelligence, E2E Measurements, Machine Learning, Network Dynamics, Quality of Service Prediction