论文标题
用车辆排的车辆通信网络的基本面
Fundamentals of Vehicular Communication Networks with Vehicle Platoons
论文作者
论文摘要
车辆排成是一种有前途的方式,可以促进具有共同路线的车辆有效运动。尽管它具有相关性,但在很大程度上尚未探索等排和沟通性能的相互作用和沟通性能。受此启发的启发,我们开发了一种全面的方法,用于对流量的VNS进行统计建模和系统级分析。使用遍布良好接受的Poisson Line工艺(PLP)对道路网络进行建模,我们根据独立的Matern群集工艺(MCP)将车辆放置在每个道路上,该过程共同捕获了每个排中的柏油路和车辆位置的随机性。由此产生的三个过程过程是PLP驱动的COX过程,我们将其称为PLP-MCP。我们首先介绍了这个新的点过程的分布,并获得了由此产生的VN分析至关重要的几个基本属性。假设蜂窝基站(BSS)分布为泊松点过程(PPP),我们将得出典型BS服务的负载和与典型用户相关的BS的分布。在得出后者时,我们还提出了一种新的方法,可以在泊松伏罗尼(Poisson voronoi Tessellation)中得出标记和弦的长度分布。使用派生结果,我们在考虑BSS的部分加载时介绍了典型用户的速率覆盖率。我们还提供了对VNS的比较分析,而没有交通拥堵。
Vehicular platooning is a promising way to facilitate efficient movement of vehicles with a shared route. Despite its relevance, the interplay of platooning and the communication performance in the resulting vehicular network (VN) is largely unexplored. Inspired by this, we develop a comprehensive approach to statistical modeling and system-level analysis of VNs with platooned traffic. Modeling the network of roads using the by-now well-accepted Poisson line process (PLP), we place vehicles on each road according to an independent Matern cluster process (MCP) that jointly captures randomness in the locations of platoons on the roads and vehicles within each platoon. The resulting triply-stochastic point process is a PLP-driven-Cox process, which we term the PLP-MCP. We first present this new point process's distribution and derive several fundamental properties essential for the resulting VN's analysis. Assuming that the cellular base-stations (BSs) are distributed as a Poisson point process (PPP), we derive the distribution of the loads served by the typical BS and the BS associated with the typical user. In deriving the latter, we also present a new approach to deriving the length distribution of a tagged chord in a Poisson Voronoi tessellation. Using the derived results, we present the rate coverage of the typical user while considering partial loading of the BSs. We also provide a comparative analysis of VNs with and without platooning of traffic.