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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08851 |
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| _version_ | 1866917551604236288 |
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| author | Chou, Po-Heng Lin, Da-Chih Wei, Hung-Yu Saad, Walid Tsao, Yu |
| author_facet | Chou, Po-Heng Lin, Da-Chih Wei, Hung-Yu Saad, Walid Tsao, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks Chou, Po-Heng Lin, Da-Chih Wei, Hung-Yu Saad, Walid Tsao, Yu Networking and Internet Architecture Machine Learning Signal Processing 68T05, 90B18, 93E35 C.2.1; I.2.6; C.4 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. |
| title | Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks |
| topic | Networking and Internet Architecture Machine Learning Signal Processing 68T05, 90B18, 93E35 C.2.1; I.2.6; C.4 |
| url | https://arxiv.org/abs/2511.08851 |