预测精度足够吗？针对波束跳变低轨卫星网络流量预测器的对比研究
Is Forecasting Accuracy Enough? A Comparative Study of Traffic Forecasters for Beam-Hopping LEO Satellite Networks
- Polytechnique Montréal（蒙特利尔理工学院）
- MDA Space（MDA太空）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究对比波束跳变低轨卫星网络的多种流量预测模型，发现预测精度差异对系统性能影响极小，建议优化利用率裕度和规划周期而非追求精度边际增益。
AI中文摘要：
我们评估了波束跳变（BH）低轨（LEO）卫星网络中用户流量需求预测的多种模型，质疑预测精度是否为该任务的合适目标。为捕捉用户流量需求的复杂特性，我们采用二阶自相似流量模型，并补充公开可用的Wi-Fi数据集，以验证该自相似模型是否符合实际流量模式。我们对比的预测器涵盖从经典统计方法（如自相似数据的最优预测器、离散采样网格上的最优线性预测器），到分整自回归移动平均（FARIMA）模型，再到新兴深度学习架构；后者包括基础与领域特定的Transformer模型，以及仅由线性层构成的轻量级神经网络。我们在两个层面评估这些模型：孤立层面通过平均绝对比例误差（MASE），系统层面通过BH模拟器（预测结果驱动波束照射计划）。在纯自相似流量下，三种感知自相似性的预测器表现相当，且优于学习型模型；而在原始Wi-Fi轨迹上，该排序几乎反转，因季节性违背了它们的平稳增量假设，移除周期分量可恢复其对比精度。关键的是，这些精度差异几乎不会传递到系统层面：丢包率和缓冲区积压更多受系统利用率和规划周期影响，而非预测器的选择，当利用率低于0.90时，预测器间的性能差距完全消失。这表明设计工作应更专注于优化利用率裕度和规划周期，而非追求原始精度的边际增益。
英文摘要：
We evaluate diverse models for user traffic demand forecasting in Low Earth Orbit (LEO) satellite networks with Beam Hopping (BH), questioning whether predictive accuracy is the right objective for this task. To capture the complex nature of the user traffic demand, we employ a second-order self-similar traffic model, supplemented by a publicly available Wi-Fi dataset to validate the self-similar model against the empirical traffic patterns. We compare forecasters ranging from classical statistical approaches, such as the optimal forecaster for self-similar data and the optimal linear predictor on the discrete sampling grid, to Fractional Auto-Regressive Integrated Moving Average (FARIMA) models, as well as emerging deep learning architectures. The latter category encompasses foundation and domain-specific transformer models, alongside a lightweight neural network consisting solely of linear layers. We assess these models at two levels: in isolation, through the Mean Absolute Scaled Error (MASE), and in context, through a BH simulator in which the forecast drives the illumination plan. On purely self-similar traffic the three self-similarity aware forecasters perform on par with one another and dominate the learned models, whereas on the raw Wi-Fi trace this ordering nearly reverses. Seasonality violates their stationary increment assumption; removing the periodic component restores their comparative accuracy. Crucially, these accuracy differences barely propagate to the system level. Loss ratio and buffer backlog are affected more by system utilization and the planning period than by the choice of forecaster, with the performance gap between forecasters vanishing entirely below 0.90 utilization. This suggests design efforts are better spent optimizing utilization margins and planning periods rather than chasing marginal gains in raw accuracy.
