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Modeling and Analysis of GEO Satellite Networks

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arxiv 2312.15924 v1 pith:RE3GBBE5 submitted 2023-12-26 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords satellitessatellitecoveragenetworksanalyticalderivedistributionearth
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The extensive coverage offered by satellites makes them effective in enhancing service continuity for users on dynamic airborne and maritime platforms, such as airplanes and ships. In particular, geosynchronous Earth orbit (GEO) satellites ensure stable connectivity for terrestrial users due to their stationary characteristics when observed from Earth. This paper introduces a novel approach to model and analyze GEO satellite networks using stochastic geometry. We model the distribution of GEO satellites in the geostationary orbit according to a binomial point process (BPP) and examine satellite visibility depending on the terminal's latitude. Then, we identify potential distribution cases for GEO satellites and derive case probabilities based on the properties of the BPP. We also obtain the distance distributions between the terminal and GEO satellites and derive the coverage probability of the network. We further approximate the derived expressions using the Poisson limit theorem. Monte Carlo simulations are performed to validate the analytical findings, demonstrating a strong alignment between the analyses and simulations. The simplified analytical results can be used to estimate the coverage performance of GEO satellite networks by effectively modeling the positions of GEO satellites.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Non-Terrestrial Network Models Using Stochastic Geometry: Planar or Spherical?

    cs.NI 2025-07 conditional novelty 6.0 of 10

    A stochastic geometry framework that generates paired planar and spherical point processes, quantifies their relative error, and gives an optimal planar altitude for approximating non-terrestrial networks.

  2. Modeling and Analysis of Non-Terrestrial Networks by Spherical Stochastic Geometry

    cs.NI 2025-01 conditional novelty 3.0 of 10

    A survey of spherical stochastic geometry for non-terrestrial networks that introduces a new orbital point process (DSBPP) and quantitative case studies on planar approximation and satellite positioning.

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