Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis

Mustafa Abdelsalam Kishk; Mohamed-Slim Alouini

doi:10.1109/lcomm.2022.3194210

Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis

Mustafa Abdelsalam Kishk, Mohamed-Slim Alouini

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

Abstract

This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm’s performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.

Original language	English (US)
Pages (from-to)	1-1
Number of pages	1
Journal	IEEE Communications Letters
DOIs	https://doi.org/10.1109/lcomm.2022.3194210
State	Published - Jul 27 2022

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KAUST Repository Item: Exported on 2022-09-14

ASJC Scopus subject areas

Modeling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/lcomm.2022.3194210

https://repository.kaust.edu.sa/bitstream/10754/679914/1/Evaluating_the_Accuracy_of_Stochastic_Geometry_Based_Models_for_LEO_Satellite_Networks_Analysis.pdf

Cite this

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title = "Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis",

abstract = "This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm{\textquoteright}s performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.",

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PY - 2022/7/27

Y1 - 2022/7/27

N2 - This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm’s performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.

AB - This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm’s performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.

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JF - IEEE Communications Letters

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Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis

Abstract

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ASJC Scopus subject areas

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