Modeling and Analysis for Multiple-Layer LEO Satellite Internet of Things Constellations

Jul 16, 2026

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Multiple-satellite low Earth orbit (LEO) Internet of Things (IoT) constellations rely on multiple-layer orbits with different altitudes to provide global coverage. Performance in practical Rician fading channels is insufficiently understood because existing modeling becomes analytically intractable. This work develops a stochastic-geometry framework that combines Rician channel modeling with Cox point processes and introduces a new channel approximation. It yields closed-form connectivity, coverage, and transmission-rate metrics, validated by extensive simulations, and provides design guidance for deployment optimization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

Introduction

Motivation for multi-layer LEO IoT constellations

Need for tractable performance modeling

Stochastic geometry and point-process foundations

Modeling and Analysis for Multiple-Layer LEO Satellite Internet of Things Constellations Ming Ying, Xiaoming Chen, Qiao Qi, and Yichao Xu arXiv :2607 .09035v 1 [ cs .IT] 10 Jul 2026 Abstract—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 geometrybased 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 shortpacket 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. Index Terms—Internet of Things, low Earth orbit satellite, performance analysis, system modeling, multiple-layer architecture. I. INTRODUCTION Internet of Things (IoT) has witnessed exponential growth, enabling ubiquitous connectivity across diverse applications, such as intelligent transportation, precision agriculture, and environmental monitoring [1]-[3] . These applications increasingly demand ubiquitous, reliable, and low-latency connectivity, especially in scenarios involving mobile or geographically isolated devices. However, terrestrial networks often struggle to meet these requirements due to limitations in infrastructure deployment and geographical reach. In particular, vast regions such as oceans, and deserts remain underserved by ground-based systems, where deploying cellular IoT infrastructure is neither economically viable nor technically feasible [4] . To address this gap, low Earth orbit (LEO) satellite networks have emerged as a promising complementary solution, offering global coverage, reduced latency, and robust connectivity for distributed IoT devices [5] . Unlike geostationary systems, LEO satellites operate at lower altitudes, which signiﬁcantly cuts propagation Ming Ying, Xiaoming Chen, and Yichao Xu are with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China (e-mail:{ming ying, chen xiaoming, yichao [xu](xu}@zju.edu.cn)[}](xu}@zju.edu.cn)[@zju.edu.cn](xu}@zju.edu.cn)). Qiao Qi is with the School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China (email: [qiqiao@hznu.edu.cn](qiqiao@hznu.edu.cn)). delay and power consumption, offering key advantages for energy-constrained IoT deployments [6] . Nevertheless, the dynamic characteristic of LEO constellations introduces challenges such as intermittent visibility and complex handover management, necessitating carefully designed multilayer architectures to ensure seamless service continuity. Given the substantial investment and strategic importance of LEO satellite IoT constellations, accurate performance modeling and prediction become prerequisites for viable system design and commercialization. A rigorous theoretical framework is therefore essential to guide constellation design and optimize resource allocation [8] . In this context, stochastic geometry has been proven to be a powerful tool for modeling large-scale wireless networks, providing tractable expressions fo

FAQs

Why are multiple-layer LEO satellite IoT constellations needed for global IoT coverage?

Multiple-satellite constellations use orbits at different altitudes to improve coverage and enable wide-area connectivity, including regions where ground networks are difficult to deploy.

What challenge prevents existing analysis of multi-layer LEO satellite IoT under Rician fading?

Rician fading channel modeling leads to complex theoretical expressions, making mathematical analysis difficult and performance results hard to obtain analytically.

How does the proposed framework obtain key performance metrics?

It combines Rician channel modeling with Cox point processes and uses a novel channel approximation to overcome intractable terms, then derives closed-form expressions for connectivity, coverage probability, and transmission rate, including short-packet transmission.

Jul 16, 2026

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