超越理想化轨道：面向LEO卫星星座的真实网络拓扑与时空流量仿真

中文摘要 AI 辅助

大型低地球轨道（LEO）卫星星座正通过提供潜在的高容量、低延迟通信，彻底改变全球网络接入方式，推动先进天基非地面网络架构的创新与加速研究。因此，一个允许灵活设计真实网络路由方案、流量负载均衡及壳间通信方法以模拟真实多壳层网络的研究测试平台，成为行业开发者和学术研究人员的重要工具。本研究扩展了SpaceNet测试平台的范围，现包括多壳层星座的仿真以及利用现有全球地面终端网络计算路由。此外，该测试平台通过支持高级拥塞建模及必要的局部和全局拥塞路由，能够准确生成往返时间（RTT）和吞吐量指标的趋势。为评估测试平台的有效性，将Starlink星座的仿真结果网络性能与Starlink终端间实际Ping和iPerf测试数据进行了比较和评估。本文讨论了两个重要的通信场景，凸显了测试平台在生成真实多壳层拓扑及通过信息充分的拥塞路由处理各类流量分布方面的潜力。使用SpaceNet进行的网络性能预测准确解决了不同流量水平下的时间行为问题，其吞吐量和RTT时间序列具有较强的时间可预测性，且在95%置信区间内增强了地面真值覆盖。

英文摘要

Large low-earth orbit (LEO) satellite constellations are revolutionizing network access globally by delivering potential high-capacity, low-latency communication, fueling innovation and accelerating research in advanced space-based non-terrestrial network architectures. Consequently, a research testbed that allows flexibility in designing realistic network routing schemes, traffic load balancing and inter-shell communication methods to emulate real-world multi-shell network becomes an important tool for industry developers and academic researchers. This study broadens the scope of the SpaceNet testbed, now including emulation of multi-shell constellations and computation of routes with the existing global web of terrestrial terminals. Furthermore, the testbed produces accurate trends in return trip time (RTT) and throughput metrics due to its support of high-level congestion modeling and the necessary local and global congestion routing. To evaluate the efficacy of the testbed, the network performance of emulated results for the Starlink constellation is evaluated and compared with data from real Ping and iPerf tests between Starlink terminals. Two important communication scenarios have been discussed that highlight the testbed's potential for realistic multi-shell topology generation and handling various types of traffic distribution with well-informed congestion routing. Predictions of network performance using SpaceNet accurately addresses temporal behaviors for different traffic levels with strong temporal predictability in its throughput and RTT time-series, as well as enhanced ground truth coverage within a 95% confidence interval.

发表机构

• Virginia Tech（弗吉尼亚理工大学）
• Fibertek Inc.（菲伯泰克公司）
• United States Military Academy（美国军事学院）

机构由 AI 辅助整理，请以论文原文为准。