Deciphering Region-Level Signatures from Latency Measurements in LEO Satellite Internet

• URL: http://arxiv.org/abs/2606.29324v1
• Date: Sun, 28 Jun 2026 10:35:46 GMT
• Title: Deciphering Region-Level Signatures from Latency Measurements in LEO Satellite Internet
• Abstract summary: Low-Earth orbit (LEO) satellite Internet has become an indispensable infrastructure that provide growing coverage for global users.<n>We formulate the problem of region-level latency characterization using Starlink round-trip time (RTT) measurements from the public LENS dataset.<n>We then propose a hierarchical analytical framework that transforms raw RTT sequences into multi-scale statistical features for cross-region comparison.
• Score: 22.1034989583142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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