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.

翻译：低地球轨道（LEO）卫星网络正逐渐成为关键通信基础设施，基于5G标准化的非地面网络及其与地面系统的融合被视为6G的核心特征。然而，当前LEO系统仍存在显著的延迟波动，限制了其在时延敏感服务中的适用性。本文基于500Hz实验性双向单向测量数据，对端到端延迟进行了详细的统计分析，并针对Starlink中观测到的确定性15秒周期行为提出了分段模型。我们刻画了切换引发的边界区域特征：每个周期起始阶段会产生持续约140毫秒的延迟尖峰，结束阶段则产生约75毫秒的尖峰，随后进入稳定的周期内稳态区间，这一特性使得精确的短期预测成为可能。分析表明，基于长期统计的延迟预测会导致保守估计。相比之下，通过利用周期结构、隔离边界区域，并对周期内延迟分布应用轻量级参数与非参数模型，我们实现了99%分位数延迟预测误差低于50毫秒的精度。此外，周期级延迟预测与分类能够识别无法满足应用延迟需求的即将到来周期，从而通过启用备用系统实现自适应传输策略。