A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access

TL;DR

This paper presents a C-V2X platform that combines real traffic simulation and spectrum-aware access to generate realistic data for training deep learning models, enhancing vehicular communication efficiency and spectrum management.

Contribution

It introduces an integrated platform using actual traffic simulation and spectrum-aware access to facilitate development of DL-based C-V2X algorithms and spectrum management strategies.

Findings

Platform effectively trains DL-based C-V2X algorithms.

Spectrum-aware slidelink adapts to different bands with high detection accuracy.

Validated practicality in real-world vehicular environments.

Abstract

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.