# Coverage and Rate Analysis of Downlink Cellular Vehicle-to-Everything (C-V2X) Communication

## TL;DR

This paper analyzes the downlink coverage and rate performance of a C-V2X network using advanced stochastic geometry models, providing insights into optimizing network parameters for improved connectivity without additional infrastructure.

## Contribution

It introduces a novel Cox process-based model for vehicular and RSU locations, derives coverage probabilities considering shadowing effects, and offers design insights for network optimization.

## Key findings

- Coverage probability depends on network parameters and shadowing effects.
- Increasing MBS density improves rate coverage, but bias tuning can achieve similar gains.
- Proposed approximation effectively models shadowing in Cox processes.

## Abstract

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 {\em 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.