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

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.

6G-AUTOR: Autonomic CSI-Free Transceiver via Realtime On-Device Signal Analytics

“…We choose vehicular communications (5G NR mode 2) [14] given that vehicular communications are often with more strict latency constraints. We adopt a well-known Manhattan grid model and use SUMO [18] to generate realistic trajectories of vehicles to evaluate sidelink communications performance. The SUMO step-size is set as 0.1 s, the maximum vehicle speed is 45 miles/hour, and other parameters are listed in Fig.…”

“…We choose vehicular environment to test out the spectrum recognition methods. The continuous trajectories of vehicles are generated via SUMO transportation simulation platform [18] and each vehicle will perform vehicle-to-vehicle or vehicle-to-infrastructure communications occasionally by building wireless connections on available subcarriers. Note that the considering simulations are challenging as the number of occupied subcarriers and the power of built connections may vary significantly according to the realistic trajectories generated from SUMO platform.…”