用实测数据提前秒级预警5G铁路网络可靠性崩溃
Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks
- 基于10Hz列车测量数据，对比六种学习模型预测可靠性故障
- 利用商用设备可获取的轻量特征，实现秒级提前预警
- 为智能交通控制提供实证基础，适合通信与铁道领域研究者
本文针对5G非独立组网（NSA）铁路网络的可靠性崩溃事件，开展基于实测数据的早期预警研究。基于包含服务小区和邻区信息的10 Hz列车测量轨迹，对六种典型学习模型（CNN、LSTM、XGBoost、Anomaly Transformer、PatchTST、TimesNet）在不同观察窗口和预测时长下的表现进行基准测试。本研究不提出新架构，而是构建一个测量驱动的基准体系，量化在5G NSA铁路环境下实现秒级可靠性预测的可行性与运行权衡。实验结果表明，利用商用设备可获取的轻量级无线特征，学习模型可在可靠性崩溃事件发生前数秒完成预警。该基准为感知辅助通信控制提供了洞见，并为未来移动性控制中融合感知与分析奠定实证基础。
原文摘要 · Abstract (English)
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 radio link failure (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.
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