Towards AI-Native Vehicular Communications
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| Title: | Towards AI-Native Vehicular Communications |
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| Authors: | Rizzo Gianluca, Liotou Eirini, Maret Yann, Wagen J. -F., Zugno Tommaso, Wu M., Kliks Adrian |
| Source: | 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). :1-7 |
| Publisher Information: | IEEE, 2023. |
| Publication Year: | 2023 |
| Subject Terms: | distributed learning, ITS, ML/AI, QoS-prediction, V2X communications |
| Description: | The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1109/vtc2023-spring57618.2023.10199974 |
| Rights: | STM Policy #29 |
| Accession Number: | edsair.doi.dedup.....11c2a65e07c44c7a3c05e6e762d0ec0c |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications. |
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| DOI: | 10.1109/vtc2023-spring57618.2023.10199974 |