Spatial modeling and analysis of cellular networks using the ginibre point process: A tutorial

Spatial stochastic models have been much used for perfor mance analysis of wireless communication networks. This is due to th fact that the performance of wireless networks depends on the spatial con figuration of wireless nodes and the irregularity of node locations in a rea wireless network can be captured by a spatial point process. Most work on such spatial stochastic models of wireless networks have adopted homo geneous Poisson point processes as the models of wireless node locations While this adoption makes the models analytically tractable, it assume that the wireless nodes are located independently of each other and thei spatial correlation is ignored. Recently, the authors have proposed to adop the Ginibre point process-one of the determinantal point processes-a the deployment models of base stations (BSS) in cellular networks. Th determinantal point processes constitute a class of repulsive point processe and have been attracting attention due to their mathematically interestin properties and efficient simulation methods. In this tutorial, we provide brief guide to the Ginibre point process and its variant, -Ginibre poin process, as the models of BS deployments in cellular networks and sho some existing results on the performance analysis of cellular network mod els with -Ginibre deployed BSS. The authors hope the readers to use suc point processes as a tool for analyzing various problems arising in futur cellular networks.