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[2511.08851] Learning-based Radio Link Failure Prediction Based on Measurement Dataset in Railway Environments
Summary
This article discusses a study on predicting radio link failures in railway environments using machine learning models, focusing on early warning systems based on real-time data.
Why It Matters
As railway systems increasingly rely on 5G technology, ensuring reliable communication is critical. This study provides insights into using machine learning for proactive management of radio link failures, which can enhance safety and operational efficiency in railway networks.
Key Takeaways
- The study benchmarks six machine learning models for predicting radio link failures.
- Early warning systems can anticipate reliability issues seconds in advance.
- Results highlight the importance of lightweight features available on commercial devices.
- Insights align with the 6G vision of integrating sensing and analytics into mobility control.
- Proactive strategies can improve communication reliability in railway environments.
Computer Science > Networking and Internet Architecture arXiv:2511.08851 (cs) [Submitted on 12 Nov 2025 (v1), last revised 13 Feb 2026 (this version, v3)] Title:Learning-based Radio Link Failure Prediction Based on Measurement Dataset in Railway Environments Authors:Po-Heng Chou, Da-Chih Lin, Hung-Yu Wei, Walid Saad, Yu Tsao View a PDF of the paper titled Learning-based Radio Link Failure Prediction Based on Measurement Dataset in Railway Environments, by Po-Heng Chou and 4 other authors View PDF HTML (experimental) Abstract:This paper presents a measurement-driven case study on early radio link failure (RLF) warning as device-side network sensing and analytics for proactive mobility management in 5G non-standalone (NSA) railway environments. 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 focuses on quantifying the feasibility of early warning and the trade-offs among observation context, prediction horizon, and alarm reliability under real railway mobility. Experimental results show that learning models can anticipate RLF-related reliability degradation seconds in advance using lightweight features available on commercial devices. The presented benchmark provides practical insights for sen...
