AI RESEARCH
Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks
arXiv CS.LG
•
ArXi:2511.08851v4 Announce Type: replace-cross 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.