Coverage Analysis of a Vehicular Network Modeled as Cox Process Driven by Poisson Line Process

TL;DR

This paper models vehicular networks using Cox processes driven by Poisson line processes, deriving exact coverage probabilities and analyzing how network parameters affect performance.

Contribution

It introduces a novel Cox process-based model for vehicular networks on roads and derives exact coverage probability expressions considering realistic spatial configurations.

Findings

Coverage probability depends on line and node densities.

Derived exact expressions for coverage probability with Nakagami-m fading.

Compared coverage performance with 2D PPP models, highlighting differences.

Abstract

In this paper, we consider a vehicular network in which the wireless nodes are located on a system of roads. We model the roadways, which are predominantly straight and randomly oriented, by a Poisson line process (PLP) and the locations of nodes on each road as a homogeneous 1D Poisson point process (PPP). Assuming that each node transmits independently, the locations of transmitting and receiving nodes are given by two Cox processes driven by the same PLP. For this setup, we derive the coverage probability of a typical receiver, which is an arbitrarily chosen receiving node, assuming independent Nakagami-$m$ fading over all wireless channels. Assuming that the typical receiver connects to its closest transmitting node in the network, we first derive the distribution of the distance between the typical receiver and the serving node to characterize the desired signal power. We then characterize coverage probability for this setup, which involves two key technical challenges. First, we need to handle several cases as the serving node can possibly be located on any line in the network and the corresponding interference experienced at the typical receiver is different in each case. Second, conditioning on the serving node imposes constraints on the spatial configuration of lines, which require careful analysis of the conditional distribution of the lines. We address these challenges in order to accurately characterize the interference experienced at the typical receiver. We then derive an exact expression for coverage probability in terms of the derivative of Laplace transform of interference power distribution. We analyze the trends in coverage probability as a function of the network parameters: line density and node density. We also study the asymptotic behavior of this model and compare the coverage performance with that of a homogeneous 2D PPP model with the same node density.