Large-scale spatial distribution identification of base stations in cellular networks

Yifan Zhou, Zhifeng Zhao, Yves Louet, Qianlan Ying, Rongpeng Li, Xuan Zhou, Xianfu Chen, Honggang Zhang

    Research output: Contribution to journalArticleScientificpeer-review

    12 Citations (Scopus)

    Abstract

    The performance of cellular system significantly depends on its network topology while cellular networks are undergoing a heterogeneous evolution. This promising trend introduces 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 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 discovering the inaccuracy of the 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 performance in view of 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.
    Original languageEnglish
    Pages (from-to)2987-2999
    JournalIEEE Access
    Volume3
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

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    Keywords

    • Cellular networks
    • Poisson point process
    • base station (BS) locations
    • large-scale identification
    • stochastic geometry

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