Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks

[2411.15433] Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks Skip to main content Search Submit Donate Log in Computer Science > Networking and Internet Architecture arXiv:2411.15433 (cs) [Submitted on 23 Nov 2024 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks Authors: Xiangtong Wang View PDF HTML (experimental) Abstract: This study introduces a new framework for analyzing capacity dynamics and throughput performance in Low Earth Orbit satellite networks (LSNs). It focuses on addressing critical gaps in existing models, particularly those concerning unreliable ISLs. Our work systematically resolves two inherent deficiencies in prior research: (1) the conflation of network capacity with maximum throughput, the latter being highly dependent on routing policies and thus failing to reflect the intrinsic characteristics of the system; and (2) the overestimation problem in flow network based throughput calculations, which often generate flow paths that are inconsistent with actual traffic paths. To address these issues, we develop the CAP-uLSN (Capacity under unstable LEO satellites networks) model to characterize time-varying network capacity under stochastic ISL availability. Furthermore, we propose a Monte Carlo Throughput Estimation (MCTE) framework that probabilistically evaluates aggregate throughput performance under dynamic traffic patterns and diverse routing schemes. These insights derived from the CAP-uLSN model and MCTE framework, provide theoretical guidance for optimizing routing schemes (e.g., path selection under throughput fluctuations) and designing adaptive billing models (e.g., distance-based pricing) in future LEO satellite networks. Subjects: Networking and Internet Architecture (cs.NI) Cite as: arXiv:2411.15433 [cs.NI] (or arXiv:2411.15433v3 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2411.15433 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Wang, Xiangtong etc, Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks. International Journal of Satellite Communications and Networking, 2026 Related DOI : https://doi.org/10.1002/sat.70048 Focus to learn more DOI(s) linking to related resources Submission history From: Xiangtong Wang [ view email ] [v1] Sat, 23 Nov 2024 03:21:09 UTC (1,759 KB) [v2] Sat, 25 Oct 2025 08:22:07 UTC (1,869 KB) [v3] Tue, 25 Aug 2026 03:03:56 UTC (5,792 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2024-11 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks#670306
• Delay-Aware Large-Small Model Collaboration over LEO Satellite Networks#168284
• Estimation, scalability, robustness, and coordination methods for Direct-to-Satellite IoT#378065
• Optimal Sampling and Scheduling for Remote Fusion Estimation of Correlated Wiener Processes#1013458
• HRRC on the Farm: Quantile Forecasting for Highly-Reliable Remote Control via LEO Networks#429307