Modeling and Analysis for Multiple-Layer LEO Satellite Internet of Things Constellations
It addresses the lack of performance analysis for multi-layer LEO satellite IoT constellations under realistic channel conditions, offering theoretical and practical guidance for system deployment.
This paper proposes a stochastic geometry-based framework for modeling multiple-layer LEO satellite IoT constellations under Rician fading, deriving closed-form expressions for connectivity, coverage, and transmission rate. Simulations validate the model and provide design insights.
To provide multiple-satellite coverage for global Internet of Things (IoT), a low Earth orbit (LEO) satellite IoT constellation usually contains multiple-layer orbits with different altitudes. However, the performance of multiple-layer LEO satellite IoT constellations under practical Rician fading satellite channels remains unknown due to complex theoretical modeling and intractable mathematical analysis. To address these challenges, this paper proposes a stochastic geometry-based modeling and analysis framework for multiple-layer LEO satellite IoT constellations, integrating Rician channel modeling and Cox point processes. Specifically, we introduce a novel channel approximation method to overcome the intractable expressions caused by the Rician fading. Building on this method, we derive exact closed-form expressions for key performance metrics, including connectivity probability, coverage probability, and transmission rate, especially in the case of IoT short-packet transmission. Extensive simulation results validate the accuracy and effectiveness of the proposed model and reveal significant design insights. The results not only provide new theoretical perspectives for modeling and analysis of LEO satellite IoT constellations but also offer practical guidance for system deployment and optimization.