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

Abstract

The performance of cellular system significantly depends on its network topology, while cellular networks are undergoing a heterogeneous evolution. This promising trend introduces the unplanned deployment of smaller base stations (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 a 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 a detailed spatial analysis to each subset. After discovering the inaccuracy of the Poisson point process in BS locations modeling, we consider the Gibbs point processes as well as Neyman-Scott point processes and compare their performance in the view of a large-scale modeling test, and finally reveal the general clustering nature of BSs deployment. This paper carries out the first large-scale identification regarding available literature, and provides more realistic and general results to contribute to the performance analysis for the forthcoming heterogeneous cellular networks.