In this paper, we present the downlink coverage and rate analysis of a cellular vehicle-to-everything (C-V2X) communication network where the locations of vehicular nodes and road side units (RSUs) are modeled as Cox processes driven by a Poisson line process (PLP) and the locations of cellular macro base stations (MBSs) are modeled as a 2D Poisson point process (PPP). Assuming a fixed selection bias and maximum average received power based association, we compute the probability with which a typical receiver, an arbitrarily chosen receiving node, connects to a vehicular node or an RSU and a cellular MBS. For this setup, we derive the signal-to-interference ratio (SIR)-based coverage probability of the typical receiver. One of the key challenges in the computation of coverage probability stems from the inclusion of shadowing effects. As the standard procedure of interpreting the shadowing effects as random displacement of the location of nodes is not directly applicable to the Cox process, we propose an approximation of the spatial model inspired by the asymptotic behavior of the Cox process. Using this asymptotic characterization, we derive the coverage probability in terms of the Laplace transform of interference power distribution. Further, we compute the downlink rate coverage of the typical receiver by characterizing the load on the serving vehicular nodes or RSUs and serving MBSs. We also provide several key design insights by studying the trends in the coverage probability and rate coverage as a function of network parameters. We observe that the improvement in rate coverage obtained by increasing the density of MBSs can be equivalently achieved by tuning the selection bias appropriately without the need to deploy additional MBSs.

中文翻译：

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下行蜂窝车对一切 (C-V2X) 通信的覆盖率和速率分析

在本文中，我们介绍了蜂窝车对一切 (C-V2X) 通信网络的下行链路覆盖和速率分析，其中车辆节点和路侧单元 (RSU) 的位置被建模为由泊松线驱动的 Cox 过程过程 (PLP) 和蜂窝宏基站 (MBS) 的位置被建模为二维泊松点过程 (PPP)。假设一个固定的选择偏差和基于最大平均接收功率的关联，我们计算一个典型的接收器，一个任意选择的接收节点，连接到一个车载节点或一个 RSU 和一个蜂窝 MBS 的概率。对于此设置，我们推导出典型接收器的基于信号干扰比 (SIR) 的覆盖概率。计算覆盖概率的关键挑战之一源于包含阴影效应。由于将阴影效应解释为节点位置的随机位移的标准程序不能直接适用于 Cox 过程，因此我们提出了一种受 Cox 过程渐近行为启发的空间模型的近似值。使用这种渐近特征，我们根据干扰功率分布的拉普拉斯变换推导出覆盖概率。此外，我们通过表征服务车辆节点或 RSU 和服务 MBS 上的负载来计算典型接收器的下行链路速率覆盖。我们还通过研究作为网络参数函数的覆盖概率和速率覆盖的趋势，提供了几个关键的设计见解。