Predictive QoS for tele-operated driving over 5G SA networks: an experimental study
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| Název: | Predictive QoS for tele-operated driving over 5G SA networks: an experimental study |
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| Autoři: | Bréhon--Grataloup, Lucas, Kacimi, Rahim |
| Přispěvatelé: | Institut Polytechnique de Paris (IP Paris), Département Réseaux et Services de Télécommunications (TSP - RST), Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP), Institut Mines-Télécom Paris (IMT)-Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris), Network Systems and Services (NeSS-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom Paris (IMT)-Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP), Temps Réel dans les Réseaux et Systèmes (IRIT-T2RS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse - Jean Jaurès (UT2J), Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse III - Paul Sabatier (UT3), Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse III - Paul Sabatier (UT3), Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse Capitole (UT Capitole), Communauté d'universités et établissements de Toulouse (Comue de Toulouse), IEEE |
| Zdroj: | 34th International Conference on Computer Communications and Networks (ICCCN) https://hal.science/hal-05112420 34th International Conference on Computer Communications and Networks (ICCCN), IEEE, Aug 2025, Tokyo, Japan. ⟨10.1109/ICCCN65249.2025.11133812⟩ http://www.icccn.org/index.html |
| Informace o vydavateli: | CCSD IEEE |
| Rok vydání: | 2025 |
| Sbírka: | Université Toulouse 2 - Jean Jaurès: HAL |
| Témata: | Field trial, Recurrent Neural Networks, Deep learning, Connected Autonomous Vehicles, DSRC, C-V2X, 5G SA, Multi-RAT, Predictive QoS, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] |
| Geografické téma: | Tokyo, Japan |
| Popis: | International audience ; Vehicular connectivity is becoming an integral part of the automotive industry, pushed forth by edge computing and smart cities. However, in urban environments, wireless links are prone to physical disturbances. To maintain their service with these conditions, vehicular networking architectures need the ability to anticipate phenomena and adapt proactively. The multitude of variables involved in this problem calls for deep learning approaches. As such, predictive Quality-of-Service has shown promising contributions to the performance of low-latency infrastructures, but a field study of appropriate recurrent neural networks has yet to be conducted for vehicular networks. Moreover, the inherent heterogeneity of urban networks can be leveraged for service continuity purposes, by developing a radio access technology selection algorithm based on the predictions made for each technology. In that regard, while standalone 5G has been publicized as a key technology for very demanding applications like tele-operated driving, previous works have not studied its contribution in a real-life environment. This work thereby proposes a predictive Quality-of-Service infrastructure with proactive radio access technology selection for urban vehicular networks, evaluated by field trial and involving 5G SA, C-V2X and DSRC. Experimental observations show the enhanced contribution of 5G SA, with a minimal latency of 16 milliseconds and higher overall reliability than device-to-device communications. These measurements pave the way to cellular vehicular communications in applications with high service level requirements. Moreover, our system is able to ensure 99.9% success rates from 72% of the vehicle travel time to more than 90% of the time. Average reliability increases from 95.7% to 99.4%. |
| Druh dokumentu: | conference object |
| Jazyk: | English |
| DOI: | 10.1109/ICCCN65249.2025.11133812 |
| Dostupnost: | https://hal.science/hal-05112420 https://hal.science/hal-05112420v1/document https://hal.science/hal-05112420v1/file/ICCCN_25___Predictive_QoS_for_Tele_Operated_Driving.pdf https://doi.org/10.1109/ICCCN65249.2025.11133812 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| Přístupové číslo: | edsbas.78F59508 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | International audience ; Vehicular connectivity is becoming an integral part of the automotive industry, pushed forth by edge computing and smart cities. However, in urban environments, wireless links are prone to physical disturbances. To maintain their service with these conditions, vehicular networking architectures need the ability to anticipate phenomena and adapt proactively. The multitude of variables involved in this problem calls for deep learning approaches. As such, predictive Quality-of-Service has shown promising contributions to the performance of low-latency infrastructures, but a field study of appropriate recurrent neural networks has yet to be conducted for vehicular networks. Moreover, the inherent heterogeneity of urban networks can be leveraged for service continuity purposes, by developing a radio access technology selection algorithm based on the predictions made for each technology. In that regard, while standalone 5G has been publicized as a key technology for very demanding applications like tele-operated driving, previous works have not studied its contribution in a real-life environment. This work thereby proposes a predictive Quality-of-Service infrastructure with proactive radio access technology selection for urban vehicular networks, evaluated by field trial and involving 5G SA, C-V2X and DSRC. Experimental observations show the enhanced contribution of 5G SA, with a minimal latency of 16 milliseconds and higher overall reliability than device-to-device communications. These measurements pave the way to cellular vehicular communications in applications with high service level requirements. Moreover, our system is able to ensure 99.9% success rates from 72% of the vehicle travel time to more than 90% of the time. Average reliability increases from 95.7% to 99.4%. |
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| DOI: | 10.1109/ICCCN65249.2025.11133812 |