Abstract
The rapid advancement of digital technology has fueled growth in vehicle networking, enabling wireless communication between vehicles. More vehicle use cases for networked vehicles have recently been planned but are not concerned with road safety. To serve advanced use cases for connected autonomous driving applications demanding reliability and latency requirements, 5G NR Vehicle to Everything (V2X) developed as a major enabler. This paper aims to investigate the aperiodic data traffic types in 5G NR V2X resource allocation in Mode 2 using the Sensing Base Semi-Persistent Scheduling (SB SPS). A thorough simulation-based analysis was conducted to collect the results, specifically focusing on the correlation between the Packet Reception Ratio (PRR) and the dual factors of traffic density and distance. The simulations showed that higher vehicle density led to more traffic, increasing the risk of collisions and interference. As a consequence of this, PRRs dropped across the range. Hence, a suggestion for further research involves utilizing machine learning algorithms to analyze traffic patterns and demands, which can help in allocating resources more proactively.