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.

Towards AI-Native Vehicular Communications

Rizzo Gianluca;
2023-01-01

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.
2023
97th IEEE Vehicular Technology Conference, VTC 2023-Spring
ita
2023
IEEE Vehicular Technology Conference
Institute of Electrical and Electronics Engineers Inc.
2023-
1
7
distributed learning; ITS; ML/AI; QoS-prediction; V2X communications
Rizzo Gianluca; Liotou Eirini; Maret Yann; Wagen J. -F.; Zugno Tommaso; Wu M.; Kliks Adrian
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2077262
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