arXiv:2511.08851v5 Announce Type: replace-cross 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 feasibili
The proliferation of 5G infrastructure, especially in critical applications like railway networks, necessitates robust reliability monitoring and predictive maintenance solutions, which this study addresses. The timing aligns with the ongoing global rollout and refinement of 5G non-standalone networks.
Ensuring the reliability of 5G networks in railway systems is critical for safety, operational efficiency, and the adoption of autonomous rail technologies. Early warning systems can prevent costly disruptions and enhance public confidence in these advanced communication infrastructures.
This research provides a benchmark for machine learning models to detect 5G network reliability breakdowns in real-time, moving from reactive maintenance to proactive intervention. It quantifies the feasibility of using existing measurement data for predictive monitoring rather than proposing new architectural designs.
- · Telecommunications infrastructure providers
- · Railway operators
- · AI/ML model developers
- · Smart city solution providers
- · Legacy railway communication systems
- · Manual network monitoring services
Increased reliability and safety of 5G-enabled railway operations through predictive maintenance.
Reduced operational costs for railway companies due to fewer unplanned outages and optimized maintenance schedules.
Accelerated adoption of more advanced autonomous services in railway systems, potentially influencing logistics and public transport paradigms.
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