Statistical Characterization and Prediction of E2E Latency over LEO Satellite Networks

2026-01-13

AI Summary

This work addresses the challenge of severe end-to-end latency fluctuations in low Earth orbit (LEO) satellite networks, which hinder support for delay-sensitive applications. Leveraging 500 Hz real-world measurements, it reveals—for the first time—a deterministic 15-second periodic structure in Starlink latency. Building on this insight, the study proposes a segmented modeling approach that distinguishes boundary regions caused by satellite handovers from stable intervals. By isolating boundary effects and combining lightweight parametric and non-parametric models, the method achieves short-term latency prediction with sub-50 ms error at the 99th percentile. Furthermore, it enables cycle-level latency classification, facilitating adaptive transmission strategies. This framework establishes a new paradigm for high-precision latency prediction in LEO satellite networks.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• E2E latency
• LEO satellite networks
• latency prediction
• latency variation
• delay-sensitive services

Innovation

• LEO satellite networks
• end-to-end latency prediction
• periodic latency characterization
• handover-induced latency spikes
• adaptive transmission strategies