This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks. Using 10 Hz metro-train measurement traces with serving- and neighbor-cell indicators, we benchmark six representative learning models, including CNN, LSTM, XGBoost, Anomaly Transformer, PatchTST, and TimesNet, under multiple observation windows and prediction horizons. Rather than proposing a new prediction architecture, this study develops a measurement-driven benchmark to quantify the feasibility and operating trade-offs of seconds-ahead reliability prediction in 5G NSA railway environments. Experimental results show that learning models can anticipate RLF-related reliability breakdown events seconds in advance using lightweight radio features available on commercial devices. The presented benchmark provides insights for sensing-assisted communication control and offers an empirical foundation for integrating sensing and analytics into future mobility control.

翻译：本文提出了一种基于测量的5G非独立组网（NSA）铁路网络中可靠性崩溃事件的早期预警研究。利用10赫兹的地铁列车测量轨迹（包含服务小区和邻小区指标），我们在多种观测窗口和预测时间跨度下，对六种代表性学习模型进行了基准测试，包括CNN、LSTM、XGBoost、Anomaly Transformer、PatchTST和TimesNet。本研究并非提出新的预测架构，而是通过构建测量驱动的基准，量化了5G NSA铁路环境下秒级可靠性预测的可行性及操作权衡。实验结果表明，利用商用设备上可获取的轻量级无线特征，学习模型能够提前数秒预测与RLF相关的可靠性崩溃事件。本基准测试为感知辅助通信控制提供了见解，并为将感知与分析集成到未来移动控制中奠定了实证基础。