Ping Du
ABSTRACT
In the next-generation intelligent transportation system, not only conventional static information like geographic location but also various dynamic information such as vehicle mobility, traffic signals and also in-vehicle IoT sensor data needs to be collected and transferred. In this paper, we propose a deep-learning based control plane and user plane separation (CUPS) automotive edge computing architecture to offload localized mapping information to edge server to reduce the transmitted traffic volume to central server and also the response latency of automotive applications. For each automotive application, we can deploy an Evolved Packet Core (EPC) user plane on-demand. We apply deep learning to classify packets of different automotive applications to different Radio Access Networks (RAN) slices for application-specific spectrum scheduling and also route packets to different application-specific edge servers via corresponding EPC user planes.
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Index Terms
- Deep learning-based C/U plane separation architecture for automotive edge computing
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