{"ID":2842528,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08851","arxiv_id":"2511.08851","title":"Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks","abstract":"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.","short_abstract":"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 Tran...","url_abs":"https://arxiv.org/abs/2511.08851","url_pdf":"https://arxiv.org/pdf/2511.08851v4","authors":"[\"Po-Heng Chou\",\"Da-Chih Lin\",\"Hung-Yu Wei\",\"Walid Saad\",\"Yu Tsao\"]","published":"2025-11-12T00:13:37Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.LG\",\"eess.SP\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}