🤖 AI Summary
Modeling performance in low Earth orbit (LEO) satellite networks is challenging due to the inherent heterogeneity in satellite altitudes, which complicates analytical tractability.
Method: This paper proposes the first analytically tractable unified stochastic geometric model for integrated satellite-terrestrial networks: satellites and terrestrial base stations are jointly modeled as a Poisson point process (PPP) on concentric spherical surfaces, each node marked by a random height, with rigorous incorporation of line-of-sight visibility constraints.
Contribution/Results: The derived closed-form expression for coverage probability explicitly captures its joint dependence on the path-loss exponent, altitude distribution, node density, and bias factor. Quantitative analysis reveals dual gains—enhanced wide-area coverage in remote rural regions and effective traffic offloading in dense urban areas—thereby providing a theoretical foundation and design guidelines for coordinated LEO network deployment.
📝 Abstract
With the growing interest in satellite networks, satellite-terrestrial integrated networks (STINs) have gained significant attention because of their potential benefits. However, due to the lack of a tractable network model for the STIN architecture, analytical studies allowing one to investigate the performance of such networks are not yet available. In this work, we propose a unified network model that jointly captures satellite and terrestrial networks into one analytical framework. Our key idea is based on Poisson point processes distributed on concentric spheres, assigning a random height to each point as a mark. This allows one to consider each point as a source of desired signal or a source of interference while ensuring visibility to the typical user. Thanks to this model, we derive the probability of coverage of STINs as a function of major system parameters, chiefly path-loss exponent, satellites and terrestrial base stations' height distributions and density, transmit power and biasing factors. Leveraging the analysis, we concretely explore two benefits that STINs provide: i) coverage extension in remote rural areas and ii) data offloading in dense urban areas.
