论文标题
在$ k $近距离路径上，距曼哈顿泊松线路的典型交叉口cox流程
On the $k$ Nearest-Neighbor Path Distance from the Typical Intersection in the Manhattan Poisson Line Cox Process
论文作者
论文摘要
在本文中，我们考虑了由曼哈顿泊松线过程驱动的COX点过程。我们计算随机选择的交叉点和COX过程的$ k $ th的路径距离（L1规范）的确切累积分布函数（CDF）。 CDF表示为整数分区函数$ p \！\ left（k \ right）$的总和，它使我们能够以简单的方式以$ k $的实际值来数字评估CDF。这些距离分布可用于研究从位于智能运输系统（ITS）的交叉路口传输的广播信号的$ K $覆盖。同样，如果将\ ac {rsu}放置在交叉点上，则它们可以对车辆到所有设施（V2X）系统的网络尺寸（V2X）系统具有深刻的看法。最后，他们可以在其他科学分支中找到有用的应用程序，例如空间数据库，紧急响应计划和地区。我们使用市区地图来证实距离分配模型的适用性。
In this paper, we consider a Cox point process driven by the Manhattan Poisson line process. We calculate the exact cumulative distribution function (CDF) of the path distance (L1 norm) between a randomly selected intersection and the $k$-th nearest node of the Cox process. The CDF is expressed as a sum over the integer partition function $p\!\left(k\right)$, which allows us to numerically evaluate the CDF in a simple manner for practical values of $k$. These distance distributions can be used to study the $k$-coverage of broadcast signals transmitted from a \ac{RSU} located at an intersection in intelligent transport systems (ITS). Also, they can be insightful for network dimensioning in vehicle-to-everything (V2X) systems, because they can yield the exact distribution of network load within a cell, provided that the \ac{RSU} is placed at an intersection. Finally, they can find useful applications in other branches of science like spatial databases, emergency response planning, and districting. We corroborate the applicability of our distance distribution model using the map of an urban area.
