Stochastic Geometry Modeling of Cellular Networks: Analysis, Simulation and Experimental Validation

TL;DR

This paper evaluates the accuracy of stochastic geometry models for cellular networks, incorporating real-world data on base station locations, buildings, and antenna patterns, demonstrating their effectiveness in dense urban settings.

Contribution

It provides an empirical validation of stochastic geometry models against real urban data, enhancing their credibility for network analysis and planning.

Findings

Stochastic geometry models accurately predict network performance in dense urban areas.

Inclusion of real building footprints improves modeling precision.

Model validation uses UK data for base stations and urban structures.

Abstract

Due to the increasing heterogeneity and deployment density of emerging cellular networks, new flexible and scalable approaches for their modeling, simulation, analysis and optimization are needed. Recently, a new approach has been proposed: it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system-level modeling, performance evaluation and optimization. In this paper, we investigate the accuracy of this emerging abstraction for modeling cellular networks, by explicitly taking realistic base station locations, building footprints, spatial blockages and antenna radiation patterns into account. More specifically, the base station locations and the building footprints are taken from two publicly available databases from the United Kingdom. Our study confirms that the abstraction model based on stochastic geometry is capable of accurately modeling the communication performance of cellular networks in dense urban environments.