Paper Citation Record · LEDGER

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics

As of 21 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2509.03290.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.03290 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:35:28.051015Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy12
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f9b1c073-a520-4c6b-a514-7e8c8704ceab · outbound

This paper cites A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.207374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4d9a5b91-11aa-4687-ba5b-298f8f10fb81 · outbound

This paper cites Empowering the 6G Cellular Architecture With Open RAN.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Empowering the 6G Cellular Architecture With Open RAN

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.197535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0c05b40a-c15f-4e4e-8324-ca41d0972cfd · outbound

This paper cites Anomaly detection in mobile networks.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Anomaly detection in mobile networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.186937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6f1c8f9c-dd25-4e87-b235-614cc73a63af · outbound

This paper cites Anomaly detection and root cause analysis enabled by artificial intelligence.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Anomaly detection and root cause analysis enabled by artificial intelligence

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.176917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bebe5d50-ef4c-4f35-bd94-532fc33dedf2 · outbound

This paper cites Uncovering latency anomalies in 5G RAN - A combination learner approach.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Uncovering latency anomalies in 5G RAN - A combination learner approach

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.166444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 46de0a56-5d36-4f7a-ad68-8684403969ec · outbound

This paper cites Benchmarking of anomaly detection techniques in O-RAN for handover optimization.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Benchmarking of anomaly detection techniques in O-RAN for handover optimization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.156076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 55566096-6895-42ea-a4b9-5828fc05f3ec · outbound

This paper cites SpotLight: Accurate, explainable and efficient anomaly detection for Open RAN.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics SpotLight: Accurate, explainable and efficient anomaly detection for Open RAN

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.145542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 752afac6-ede1-4c48-8e6e-ca27fea9193d · outbound

This paper cites Lundberg and et al.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Lundberg and et al

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.133578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9faf4008-1219-4aae-a7ca-ba57b997e493 · outbound

This paper cites Near Real-Time RAN Intelligent Controller E2 Service Model KPM.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Near Real-Time RAN Intelligent Controller E2 Service Model KPM

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.123573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2c9310ca-03bd-4d30-b679-e3a43adeabe6 · outbound

This paper cites Towards autonomous open radio access networks.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Towards autonomous open radio access networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.113188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 53cf6406-620c-4a85-9809-8e0d76445153 · outbound

This paper cites Scalable and interpretable one-class svms with deep learning and random fourier features.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics Scalable and interpretable one-class svms with deep learning and random fourier features

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.102405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 009a948b-e7a6-43b7-98c6-5d199fa45fc1 · outbound

This paper cites O-RAN-SC GitHub Page.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics O-RAN-SC GitHub Page

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:35:28.090708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 670c335a-13c3-4236-948d-be065c62b46e · outbound

This paper cites PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection.

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T16:35:28.051015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.