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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.17689 |
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| _version_ | 1866914693136777216 |
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| author | Ain, Noor Ul Hernangómez, Rodrigo Palaios, Alexandros Kasparick, Martin Stańczak, Sławomir |
| author_facet | Ain, Noor Ul Hernangómez, Rodrigo Palaios, Alexandros Kasparick, Martin Stańczak, Sławomir |
| contents | Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving, platooning, cooperative maneuvering, teleoperated driving, and smart navigation. These and other use cases often rely on specific quality of service (QoS) levels for communication. Recently, the area of predictive quality of service (QoS) has received a great deal of attention as a key enabler to forecast communication quality well enough in advance. However, predicting QoS in a reliable manner is a notoriously difficult task. In this paper, we evaluate ML tree-ensemble methods to predict QoS in the range of minutes with data collected from a cellular test network. We discuss radio environment characteristics and we showcase how these can be used to improve ML performance and further support the uptake of ML in commercial networks. Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles. Moreover, we are extending prior art by showing how longer prediction horizons can be supported. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QoS prediction in radio vehicular environments via prior user information Ain, Noor Ul Hernangómez, Rodrigo Palaios, Alexandros Kasparick, Martin Stańczak, Sławomir Machine Learning Artificial Intelligence Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving, platooning, cooperative maneuvering, teleoperated driving, and smart navigation. These and other use cases often rely on specific quality of service (QoS) levels for communication. Recently, the area of predictive quality of service (QoS) has received a great deal of attention as a key enabler to forecast communication quality well enough in advance. However, predicting QoS in a reliable manner is a notoriously difficult task. In this paper, we evaluate ML tree-ensemble methods to predict QoS in the range of minutes with data collected from a cellular test network. We discuss radio environment characteristics and we showcase how these can be used to improve ML performance and further support the uptake of ML in commercial networks. Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles. Moreover, we are extending prior art by showing how longer prediction horizons can be supported. |
| title | QoS prediction in radio vehicular environments via prior user information |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.17689 |