Statistical Characterization and Prediction of E2E Latency over LEO Satellite Networks

TL;DR

This paper analyzes and predicts end-to-end latency in LEO satellite networks, revealing periodic behavior and boundary effects, and develops models for accurate short-term latency prediction to improve latency-sensitive applications.

Contribution

It introduces a detailed statistical analysis of LEO satellite latency, segments periodic behavior, and proposes lightweight models for precise short-term latency prediction.

Findings

Latency spikes occur at cycle boundaries due to handovers.

Intra-period latency can be accurately predicted with lightweight models.

Period-level analysis enables adaptive strategies for latency-critical applications.

Abstract

Low Earth Orbit (LEO) satellite networks are emerging as an essential communication infrastructure, with standardized 5G-based non-terrestrial networks and their integration with terrestrial systems envisioned as a key feature of 6G. However, current LEO systems still exhibit significant latency variations, limiting their suitability for latency-sensitive services. We present a detailed statistical analysis of end-to-end latency based on 500Hz experimental bidirectional one-way measurements and introduce a segmentation of the deterministic 15-second periodic behavior observed in Starlink. We characterize handover-induced boundary regions that produce latency spikes lasting approximately 140 ms at the beginning and 75 ms at the end of each cycle, followed by a stable intra-period regime, enabling accurate short-term prediction. This analysis shows that latency prediction based on long-term statistics leads to pessimistic estimates. In contrast, by exploiting the periodic structure, isolating boundary regions, and applying lightweight parametric and non-parametric models to intra-period latency distributions, we achieve 99th-percentile latency prediction errors below 50 ms. Furthermore, period-level latency prediction and classification enable adaptive transmission strategies by identifying upcoming periods where application latency requirements cannot be satisfied, necessitating the use of alternative systems.