2021 article

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

2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), pp. 1293–1298.

By: C. Lin*, S. Lin* , C. Wang & T. Chase*

TL;DR: A cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access that can effectively train and realize DL-based C-V1X algorithms and validates its practicality in real-world vehicular environments. (via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (OpenAlex)
Source: Web Of Science
Added: July 5, 2022

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 scheme can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.