AI 中文总结
该研究针对基于DVB的波束跳变低轨卫星网络，评估两种需求预测方案对时延和抖动的影响，发现预测驱动的动态规划可降低时延，且优先系统可扩展性比提升预测准确性更具价值。
AI 中文摘要
在采用波束跳变（BH）的低轨（LEO）卫星网络中，资源分配计划必须提前确定。这种固有的操作延迟使得在规划阶段必须预测未来的用户需求，这种预测灵活性对于军事应用尤为关键，因为不可预测的战术环境需要低延迟、高弹性的通信链路。然而，现有的预测模型通常仅基于独立的准确性进行评估，忽略了它们对整体网络性能的跨层影响。为解决这一差距，我们在符合DVB-S2X标准的全面全栈低轨卫星仿真环境中评估了两种不同的需求预测方案。除了预测准确性外，我们还研究了将用户需求预测纳入波束跳变计划生成过程如何影响关键网络指标，尤其是时延和抖动。我们将这些预测方案与静态分配基线进行了评估。结果表明，在某些负载条件下，基于预测的动态规划相比静态分配方法，各波束的时延降低了10%-40%。重要的是，预测准确性的边际提升并不会转化为成比例的网络指标增益。尽管所评估的预测方案在归一化均方误差（NMSE）上相差14%-16%，但这种差异带来的时延降低不到1%，且产生的抖动特性几乎相同。这些发现表明，在为实际低轨部署设计用户需求预测方案时，优先考虑系统可扩展性可能比追求微小的准确性提升更有价值。
英文摘要
In LEO satellite networks utilizing beam hopping (BH), resource allocation plans must be committed well in advance. This inherent operational delay necessitates predicting future user demand during the planning phase. Such predictive agility is particularly crucial for military applications, where unpredictable tactical environments demand low-latency, resilient communication links. However, existing forecasting models are typically evaluated based on standalone accuracy, ignoring their cross-layer impact on overall network performance. To address this gap, we evaluate two distinct demand forecasting solutions within a comprehensive, full-stack LEO satellite simulation compliant with DVB-S2X standards. Beyond prediction accuracy, we examine how incorporating user demand forecasts into BH plan generation impacts key network metrics, particularly delay and jitter. We evaluate these forecasting solutions alongside a static allocation baseline. Our results demonstrate that forecast-based dynamic planning reduces delay by 10-40% across the beams under certain load conditions compared to static allocation methods. Crucially, marginal improvements in predictive accuracy do not translate into proportional network metric gains. While the evaluated forecasting solutions differ by 14-16% in Normalized Mean Square Error (NMSE), this discrepancy yields less than a 1% reduction in delay and produces nearly identical jitter characteristics. These findings suggest that when designing user demand forecasting solutions for practical LEO deployments, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements.
CommentsAccepted to main track of IEEE MILCOM 2026 (National Capital Region, USA)
