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.

翻译：本研究提出了一种分析低地球轨道卫星网络容量动态与吞吐量性能的新框架，重点关注现有模型中关于不可靠星间链路的重大缺陷。我们的工作系统性地解决了先前研究中的两个固有不足：(1) 将网络容量与最大吞吐量混为一谈，而后者高度依赖路由策略，因而无法反映系统的本质特性；(2) 基于流网络的吞吐量计算中存在高估问题，其生成的流路径常与实际业务路径不符。为解决这些问题，我们建立了CAP-uLSN模型（不稳定低地球轨道卫星网络下的容量模型）以刻画随机星间链路可用性下的时变网络容量。此外，我们提出了蒙特卡洛吞吐量估计框架，该框架能够概率性地评估动态业务模式与多样化路由方案下的聚合吞吐量性能。基于CAP-uLSN模型与MCTE框架所得的研究结论，为未来低地球轨道卫星网络中优化路由方案（例如吞吐量波动下的路径选择）与设计自适应计费模型（例如基于距离的定价策略）提供了理论指导。