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Measurement-Driven Early Warning for Resilient 5G NSA Railway Networks

Chou, Po-Heng et al.
arXiv (All) · Papers
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68t05c-2-1c-4i-2-6machine-learningnetworking-and-internet-architecturesignal-processing
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, X
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