Binomial Line Cox Processes: Statistical Characterization and Applications in Wireless Network Analysis

TL;DR

This paper introduces and analyzes binomial line Cox processes as a more accurate model for street-based wireless networks, deriving key performance metrics for 5G and future systems.

Contribution

It provides the statistical characterization of binomial line processes and applies them to evaluate network performance metrics like SIR distribution and delay.

Findings

Derived the meta distribution of SIR for binomial line Cox processes.

Analyzed mean local delay and successful transmission density.

Showed improved modeling accuracy over Poisson line models.

Abstract

The current analysis of wireless networks whose transceivers are confined to streets is largely based on Poissonian models, such as Poisson line processes and Poisson line Cox processes. We demonstrate important scenarios where a model with a finite and deterministic number of streets, termed binomial line process, is more accurate. We characterize the statistical properties of the BLP and the corresponding binomial line Cox process and apply them to analyze the performance of a network whose access points are deployed along the streets of a city. Such a deployment scenario will be typical for 5G and future wireless networks. In order to obtain a fine-grained insight into the network performance, we derive the meta distribution of the signal-to-interference and noise ratio. Accordingly, we investigate the mean local delay in transmission and the density of successful transmission. These metrics, respectively, characterize the latency and coverage performance of the network and are key performance indicators of next-generation wireless systems.