想知道 Starlink 在不同地区延迟差异的原因？这篇论文用数据和机器学习告诉你答案，还给出了 83% 准确率的模型。
论文利用 LENS 数据集中的 Starlink RTT 测量数据，提出一个层级分析框架，将原始 RTT 序列转化为多尺度统计特征以进行跨区域比较。基于五个地理代表性区域的数据，发现延迟差异与基础设施可用性和 Starlink 天线到 PoP 距离强相关。互信息分析确认最小 RTT 为最具区分度的特征，XGBoost 特征重要性进一步支持该结论。模型在短期数据上达到 83% 准确率，但长期泛化能力下降，表明需要自适应模型。
Deciphering Region-Level Signatures from Latency Measurements in LEO Satellite Internet
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.