A case study on regularity in cellular network deployment

TL;DR

This study validates the $eta$-Ginibre point process as an effective model for cellular base station distribution, demonstrating its ability to fit real data and analyze deployment strategies through simulations and superposition properties.

Contribution

The paper introduces the $eta$-Ginibre point process as a flexible model for cellular network deployment, supported by real data fitting and analysis of superposition effects.

Findings

Base station locations fit well with the $eta$-Ginibre model.

Superposition of base stations tends to a Poisson process.

Deployment strategies are qualitatively interpreted from the model.

Abstract

This paper aims to validate the $\beta$-Ginibre point process as a model for the distribution of base station locations in a cellular network. The $\beta$-Ginibre is a repulsive point process in which repulsion is controlled by the $\beta$ parameter. When $\beta$ tends to zero, the point process converges in law towards a Poisson point process. If $\beta$ equals to one it becomes a Ginibre point process. Simulations on real data collected in Paris (France) show that base station locations can be fitted with a $\beta$-Ginibre point process. Moreover we prove that their superposition tends to a Poisson point process as it can be seen from real data. Qualitative interpretations on deployment strategies are derived from the model fitting of the raw data.