通过星链时延数据，识别不同地区特有的网络性能特征。
Deciphering Region-Level Signatures from Latency Measurements in LEO Satellite Internet
- 构建分层分析框架，将原始时延数据转为多尺度统计特征。
- 发现最小往返时延是区分区域的关键特征，短期预测准确率达83%。
- 适合关注低轨卫星网络性能分析的研究者和运维人员。
低地球轨道（LEO）卫星互联网已成为全球用户日益依赖的基础设施。尽管已有大量测量工作，但区域级性能特征的形成机制仍不明确，限制了在动态网络条件下识别特定区域时延特征的能力。本文基于公开的LENS数据集中的星链往返时延（RTT）测量数据，提出一种区域级时延表征方法。我们构建了分层分析框架，将原始RTT序列转化为多尺度统计特征以支持跨区域比较。利用五个地理代表性区域的数据，我们发现时延差异与部署因素密切相关，尤其是基础设施可用性和星链终端至接入点距离。互信息分析表明最小RTT是最具区分性的特征，该结论得到基于XGBoost的特征重要性分析的支持。所提模型在短期数据上达到83%的准确率，但在更长周期内性能下降，表明其时间泛化能力有限，亟需发展自适应模型与特征表示以实现长期性能建模。
原文摘要 · Abstract (English)
Low-Earth orbit (LEO) satellite Internet has become an indispensable infrastructure that provide growing coverage for global users. Despite extensive measurement efforts, the principles underlying region-level performance characteristics remain insufficiently understood, limiting the ability to identify region-specific latency signatures under dynamic network conditions. In this paper, we formulate the problem of region-level latency characterization using Starlink round-trip time (RTT) measurements from the public LENS dataset. We then propose a hierarchical analytical framework that transforms raw RTT sequences into multi-scale statistical features for cross-region comparison. Using data from five geographically representative regions, we demonstrate that latency differences are strongly associated with deployment factors, particularly infrastructure availability and Starlink dish-to-Point-of-Presence distance. Mutual information analysis identifies minimum RTT as the most discriminative feature, which is further supported by XGBoost-based feature importance. The proposed model well achieves 83% accuracy on short-term data. However, its performance degrades over longer periods, indicating limited temporal generalization and motivating the need for adaptive models and feature representations for long-term performance in the future.
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