Abstract:Vehicular platooning refers to coordinated and close movement of vehicular users (VUs) traveling together along a common route segment, offering strategic benefits such as reduced fuel costs, lower emissions, and improved traffic flow. {Highways offer a natural setting for platooning due to extended travel distances, yet their potential remains underexplored, particularly in terms of communication and connectivity. Given that effective platooning relies on robust vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, understanding connectivity dynamics on highways is essential.} In this paper, we analyze the dynamics of vehicular platooning on a highway and its impact on the performance of two forms of vehicular communications -- namely V2V and V2I communication -- compared to independent vehicle movement on a highway. The vehicular networks consists of road-side units (RSUs), modeled as a 1D Poisson point process (PPP), to provide the vehicular connectivity to the VUs. VUs are modeled as 1D PPP under the non-platooned traffic scenario (N-PTS) and as a 1D Matern cluster process (MCP) under the platooned traffic scenario (PTS). We evaluate the distribution on the per-RSU load, representing the number of VUs served, for the typical and tagged RSU. Additionally, we derive coverage probability (CP) and rate coverage (RC), which measures the probability of the signal-to-interference-plus-noise ratio (SINR) and achievable rate above a specified threshold at the typical VU along with their meta distribution (MD), providing a deeper understanding of the reliability and variability of these metrics across different spatial distributions of VUs and RSUs. Finally, we validate our theoretical findings through simulations and provide numerical insights into the impact of different traffic patterns on RSU load distribution, CP, and RC performance.
From: Kaushlendra Pandey [view email]
[v1]
Tue, 27 Jan 2026 08:51:50 UTC (1,450 KB)
[v2]
Sun, 19 Jul 2026 16:02:55 UTC (1,433 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。