Meta-Continual Mobility Forecasting for Proactive Handover Prediction

• URL: http://arxiv.org/abs/2512.11841v1
• Date: Wed, 03 Dec 2025 19:48:20 GMT
• Title: Meta-Continual Mobility Forecasting for Proactive Handover Prediction
• Abstract summary: Short-term mobility forecasting is a core requirement for proactive handover (HO) in cellular networks.<n>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.<n>The model is lightweight (128k parameters) and suitable for edge deployment in 5G and 6G systems.
• Score: 0.3906427348768226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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