Analytical Model of NR-V2X Mode 2 with Re-Evaluation Mechanism

TL;DR

This work tackles MAC-layer collisions in NR-V2X Mode 2 under massive and aperiodic traffic by introducing a DTMC-based analytical model that integrates CAM/DENM traffic generators, a device-level queue, and the re-evaluation mechanism. The model accommodates the all-slot sensing of re-evaluation and SPS scheduling, including a virtual state to manage cross transitions, enabling closed-form steady-state analysis for performance metrics like $P_{col}$ and $E[T]$. Numerical results show that re-evaluation can reduce resource collision probability by up to nearly an order of magnitude, but it also increases latency, with the balance depending on traffic intensity and $RRI$. The framework provides a theoretical basis for evaluating NR-V2X MAC configurations and guides practical tuning of re-evaluation timing to balance reliability and delay in real deployments, while suggesting avenues for further optimization in high-density, aperiodic traffic conditions.

Abstract

Massive message transmissions, unpredictable aperiodic messages, and high-speed moving vehicles contribute to the complex wireless environment, resulting in inefficient resource collisions in Vehicle to Everything (V2X). In order to achieve better medium access control (MAC) layer performance, 3GPP introduced several new features in NR-V2X. One of the most important is the re-evaluation mechanism. It allows the vehicle to continuously sense resources before message transmission to avoid resource collisions. So far, only a few articles have studied the re-evaluation mechanism of NR-V2X, and they mainly focus on network simulator that do not consider variable traffic, which makes analysis and comparison difficult. In this paper, an analytical model of NR-V2X Mode 2 is established, and a message generator is constructed by using discrete time Markov chain (DTMC) to simulate the traffic pattern recommended by 3GPP advanced V2X services. Our study shows that the re-evaluation mechanism improves the reliability of NR-V2X transmission, but there are still local improvements needed to reduce latency.

Figures (7)

• Figure 1: System Model
• Figure 2: Message Traffic Generator
• Figure 3: Queue Model
• Figure 4: NR-V2X Mode 2 Model with Re-Evaluation Mechanism
• Figure 5: The performance of average latency versus RRI among different scenarios.