Spatial Modeling and Analysis of Cellular Networks Using the Ginibre Point Process: A Tutorial

Naoto MIYOSHI  Tomoyuki SHIRAI  

Publication
IEICE TRANSACTIONS on Communications   Vol.E99-B   No.11   pp.2247-2255
Publication Date: 2016/11/01
Online ISSN: 1745-1345
Type of Manuscript: INVITED PAPER (Special Section on Deepening and Expanding of Information Network Science)
Category: 
Keyword: 
spatial stochastic models,  cellular networks,  spatial point processes,  Ginibre point process,  signal-to-interference-plus-noise ratio,  coverage probability,  

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Summary: 
Spatial stochastic models have been much used for performance analysis of wireless communication networks. This is due to the fact that the performance of wireless networks depends on the spatial configuration of wireless nodes and the irregularity of node locations in a real wireless network can be captured by a spatial point process. Most works on such spatial stochastic models of wireless networks have adopted homogeneous Poisson point processes as the models of wireless node locations. While this adoption makes the models analytically tractable, it assumes that the wireless nodes are located independently of each other and their spatial correlation is ignored. Recently, the authors have proposed to adopt the Ginibre point process — one of the determinantal point processes — as the deployment models of base stations (BSs) in cellular networks. The determinantal point processes constitute a class of repulsive point processes and have been attracting attention due to their mathematically interesting properties and efficient simulation methods. In this tutorial, we provide a brief guide to the Ginibre point process and its variant, α-Ginibre point process, as the models of BS deployments in cellular networks and show some existing results on the performance analysis of cellular network models with α-Ginibre deployed BSs. The authors hope the readers to use such point processes as a tool for analyzing various problems arising in future cellular networks.