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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2607. 18522v1 Announce Type: new Abstract: Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure.
arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.
arXiv:2608. 04710v1 Announce Type: cross Abstract: Integrated Sensing and Communication (ISAC) combines sensing and communication to efficiently utilize wireless resources and is emerging as a key paradigm for next-generation wireless networks.
arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.
arXiv:2606. 19821v1 Announce Type: new Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks.
arXiv:2606. 25709v1 Announce Type: new Abstract: Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees.