轻量级模型实时适应用户移动变化，提升5G/6G切换预测准确率
Meta-Continual Mobility Forecasting for Proactive Handover Prediction
- 用元学习+在线检测实现快速少量样本适应
- 零样本预测误差仅4.46米，10次样本下降至3.71米
- 适合边缘部署，显著减少切换失败和频繁切换
短期移动预测是蜂窝网络中主动切换的核心需求。现实中的移动行为高度非平稳：突然转向、速度急剧变化及不可预测的用户行为导致传统预测模型漂移，引发误判或失败切换。我们提出一种轻量级元持续预测框架，集成基于GRU的预测器、Reptile元初始化以实现快速少样本适应，以及基于EWMA的残差检测器，仅在发生漂移时触发紧凑的在线更新。在可复现的GeoLife与DeepMIMO流水线评估中，该方法在零样本设置下达到4.46米ADE和7.79米FDE，10次样本下少样本ADE优化至3.71米，并使从突发漂移中恢复的速度比离线GRU快2到3倍。应用于下游切换预测时，将F1提升至0.83，AUROC达0.90，显著降低漏切与乒乓切换事件。模型参数仅128k，适合5G与6G系统边缘部署。
原文摘要 · Abstract (English)
Short-term mobility forecasting is a core requirement for proactive handover (HO) in cellular networks. Real-world mobility is highly non-stationary: abrupt turns, rapid speed changes, and unpredictable user behavior cause conventional predictors to drift, leading to mistimed or failed handovers. We propose a lightweight meta-continual forecasting framework that integrates a GRU-based predictor, Reptile meta-initialization for fast few-shot adaptation, and an EWMA residual detector that triggers compact online updates only when drift occurs. Evaluated on a reproducible GeoLife and DeepMIMO pipeline, our method achieves 4.46 m ADE and 7.79 m FDE in zero-shot settings, improves few-shot ADE to 3.71 m at 10-shot, and enables recovery from abrupt drift about 2 to 3 times faster than an offline GRU. When applied to downstream HO prediction, the approach improves F1 to 0.83 and AUROC to 0.90, with substantial reductions in missed-HO and ping-pong events. The model is lightweight (128k parameters) and suitable for edge deployment in 5G and 6G systems.
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