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A Cellular Network Model with Ginibre Configured Base Stations

Part of: Stochastic processes Geometric probability and stochastic geometry Operations research and management science

Published online by Cambridge University Press:  22 February 2016

Naoto Miyoshi  and
Tomoyuki Shirai
Naoto Miyoshi*
Affiliation:
Tokyo Institute of Technology
Tomoyuki Shirai*
Affiliation:
Kyushu University
*
∗ Postal address: Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1-W8-52 Ookayama, Tokyo, 152-8552, Japan. Email address: [email protected]
∗∗ Postal address: Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan. Email address: [email protected]
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Abstract

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Stochastic geometry models for wireless communication networks have recently attracted much attention. This is because the performance of such networks critically depends on the spatial configuration of wireless nodes and the irregularity of the node configuration in a real network can be captured by a spatial point process. However, most analysis of such stochastic geometry models for wireless networks assumes, owing to its tractability, that the wireless nodes are deployed according to homogeneous Poisson point processes. This means that the wireless nodes are located independently of each other and their spatial correlation is ignored. In this work we propose a stochastic geometry model of cellular networks such that the wireless base stations are deployed according to the Ginibre point process. The Ginibre point process is one of the determinantal point processes and accounts for the repulsion between the base stations. For the proposed model, we derive a computable representation for the coverage probability—the probability that the signal-to-interference-plus-noise ratio (SINR) for a mobile user achieves a target threshold. To capture its qualitative property, we further investigate the asymptotics of the coverage probability as the SINR threshold becomes large in a special case. We also present the results of some numerical experiments.

Keywords

Stochastic geometry modelwireless communication networkcellular networkdeterminantal point processGinibre point processsignal-to-interference-plus-noise ratiocoverage probability

MSC classification

Primary: 60G55: Point processes
Secondary: 90B18: Communication networks 60D05: Geometric probability and stochastic geometry
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 46 , Issue 3 , September 2014 , pp. 832 - 845
Copyright
© Applied Probability Trust 

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