Large-scale Spatial Distribution Identification of Base Stations in Cellular Networks
Résumé
The performance of cellular system significantlydepends on its network topology while cellular networks areundergoing a heterogeneous evolution. This promising trendintroduces unplanned deployment of smaller base stations (BSs),thus complicating the performance evaluation even further. Inthis paper, based on large amount of real BS locations data,we present a comprehensive analysis on the spatial modeling ofcellular network structure. Unlike the related works, we dividethe BSs into different subsets according to geographical factor(e.g. urban or rural) and functional type (e.g. macrocells ormicrocells), and perform detailed spatial analysis to each subset.After recovering the inaccuracy of the Poisson point process(PPP) in BS locations modeling, we take into account the Gibbspoint processes as well as Neyman-Scott point processes andcompare their performance in view of large-scale modeling test.Finally, we turn to the BS quantitative distribution analysis,and reveal the general clustering nature of BSs deployment,which distinctly violates the traditional assumption. Specifically,the -Stable distribution can most precisely reproduce the BSdensity among the popular candidate distributions. This papercarries out a first large-scale identification regarding availableliteratures, and provides more realistic and general results tocontribute to the performance analysis for the forthcomingheterogeneous cellular networks.