A Case Study on Regularity in Cellular Network Deployment

Gomez, Jean-Sébastien; Vasseur, Aurélien; Vergne, Anaïs; Martins, Philippe; Decreusefond, Laurent; Chen, Wei

doi:10.1109/lwc.2015.2431263

Cited by 35 publications

(29 citation statements)

References 13 publications

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“…In this subsection, we use real data from the mobile network in Paris to show that base station locations can be fitted with a β-GPP [17]. We introduce the fitting method that allows to obtain the parameter β and also present the results from the fitting of each deployment and operator.…”

Section: Discussionmentioning

confidence: 99%

On a few statistical applications of determinantal point processes

et al. 2017

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Section: Discussionmentioning

confidence: 99%

On a few statistical applications of determinantal point processes

et al. 2017

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“…However, in real-life deployments, network cells are not hexagons. When considering all the frequency bands owned by an operator, network nodes are actually more similar to a Poisson point process [5]. In this case, percolation does not guarantee coverage.…”

Section: B Percolation and Coverage Holesmentioning

confidence: 99%

Building a Coverage Hole-free Communication Tree

Vergne

Decreusefond

Martins

2018

Proceedings of the 14th ACM International Symposium on QoS and Security for Wireless and Mobile Networks

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Wireless networks are present everywhere but their management can be tricky since their coverage may contain holes even if the network is fully connected. In this paper we propose an algorithm that can build a communication tree between nodes of a wireless network with guarantee that there is no coverage hole in the tree. We use simplicial homology to compute mathematically the coverage, and Prim's algorithm principle to build the communication tree. Some simulation results are given to study the performance of the algorithm and compare different metrics. In the end, we show that our algorithm can be used to create coverage hole-free communication groups with a limited number of hops.

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“…where by applying (20), the second expectation in the integrand of (30) is equal to Hence, substituting this to (30) and applying Y i ∼ Gam(i, πλ 1 /α), i ∈ N, we obtain (24c)-(24d) after some manipulations.…”

Section: A Proof Of Theoremmentioning

confidence: 99%

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

Miyoshi

Shirai

2016

IEICE Trans. Commun.

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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.

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A Case Study on Regularity in Cellular Network Deployment

Cited by 35 publications

References 13 publications

On a few statistical applications of determinantal point processes

On a few statistical applications of determinantal point processes

Building a Coverage Hole-free Communication Tree

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

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