Intent-Based Networking for Vehicular Service Deployment

Abstract

Next-generation networks $(5 \mathrm{G} / 6 \mathrm{G})$ provide capabilities such as network slicing, enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and edge computing. However, configuring the related radio, core, slicing, and edge resources requires significant operational expertise. This work proposes an Intent-Based Networking (IBN) framework that combines Natural Language Processing, Large Language Models (LLMs), TMF921-compliant intent representation, and CAMARA APIs to simplify the definition and activation of network-sensitive vehicular services. In the proposed model, a Service Provider establishes a business agreement with a Network Operator, while the operator administrator defines service requirements through a conversational interface. These requirements are translated into machine-readable intents and mapped into network and edge orchestration actions. The framework is evaluated through a Teleoperated Driving (ToD) use case for autonomous vehicle repositioning. Results show that the intent translation and management pipeline introduces limited and repeatable overhead, while orchestration time is mainly affected by the underlying MANO/IaaS platform. The results indicate that combining IBN and CAMARA APIs can support flexible service preparation by operators and dynamic service consumption by applications.