Large-scale Spatial Distribution Identification of Base Stations in Cellular Networks

TL;DR

This paper analyzes the spatial distribution of cellular base stations using large real-world data, revealing clustering patterns and evaluating different point process models to improve network performance analysis.

Contribution

It introduces a large-scale analysis dividing base stations by type and geography, and compares modeling accuracy of Poisson, Gibbs, and Neyman-Scott processes.

Findings

Poisson point process models are inaccurate for BS locations.

Base stations exhibit a general clustering pattern.

Traditional models often violate real deployment distributions.

Abstract

The performance of cellular system significantly depends on its network topology, where the spatial deployment of base stations (BSs) plays a key role in the downlink scenario. Moreover, cellular networks are undergoing a heterogeneous evolution, which introduces unplanned deployment of smaller BSs, thus complicating the performance evaluation even further. In this paper, based on large amount of real BS locations data, we present a comprehensive analysis on the spatial modeling of cellular network structure. Unlike the related works, we divide the BSs into different subsets according to geographical factor (e.g. urban or rural) and functional type (e.g. macrocells or microcells), and perform detailed spatial analysis to each subset. After examining the accuracy of Poisson point process (PPP) in BS locations modeling, we take into account the Gibbs point processes as well as Neyman-Scott point processes and compare their accuracy in view of large-scale modeling test. Finally, we declare the inaccuracy of the PPP model, and reveal the general clustering nature of BSs deployment, which distinctly violates the traditional assumption. This paper carries out a first large-scale identification regarding available literatures, and provides more realistic and more general results to contribute to the performance analysis for the forthcoming heterogeneous cellular networks.