5G-Advanced RAN KPI Root Cause Diagnosis Dataset

#root-cause diagnosis #KPI and alarm correlation analysis #configuration impact reasoning #5G-Advanced RAN performance monitoring #network fault diagnosis #radio access network optimization

5400
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

Focused on 5G-Advanced radio access network operations, this dataset provides textualized cell-level KPI observations, alarms, configuration changes, and operational context in diagnostic question-and-answer examples. Models are asked to infer the most likely issue from potentially incomplete or conflicting network evidence, explain supporting observations and uncertainty, and suggest validation steps. It supports reasoning SFT, network fault analysis, and operations-assisted diagnosis.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
network_evidence	string	Textualized cell-level KPI observations, alarms, configuration changes, and operational background provided as input evidence.
most_likely_issue	string	The principal inferred RAN problem, stated as a concise root-cause hypothesis.
diagnostic_question	string	Question asking for the most likely RAN issue and its justification based on the supplied network evidence.
supporting_evidence	string	Explanation of the observations that support the proposed root cause, including relevant KPI, alarm, configuration, or operational details.
diagnostic_reasoning	string	Reasoned account of how the evidence was weighed to select the most likely issue over alternative explanations.
recommended_validation	string	Suggested checks or measurements that could confirm or refute the inferred root cause.
conflicting_or_missing_evidence	string	Discussion of contradictory observations, incomplete evidence, and limitations that affect confidence in the root-cause hypothesis.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: Examples center on textualized network evidence and diagnostic question-and-answer pairs. They cover cell-level KPIs, alarms, configuration changes, and operational context, along with the most likely issue, supporting evidence, conflicting or missing information, diagnostic reasoning, and suggested validation steps.

Which capabilities can it support for training and evaluation?: It can support training in RAN root cause diagnosis, abductive reasoning, and evidence attribution from multiple network observations. It can also be used to evaluate how models handle incomplete or conflicting evidence.

How can the data be used in model development?: Use network evidence as input and diagnostic conclusions, reasoning, and validation recommendations as targets for reasoning SFT. The fields can also be used to build diagnostic question-and-answer evaluations or operations-assisted analysis workflows.

Where can I confirm dataset size, quality, and licensing?: Refer to the product page or data provider for the exact sample count, update status, delivery format, and license scope. Before use, review the field structure, inspect a sample of diagnostic content, and confirm that the license covers your intended training, evaluation, or commercial use.

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Cite this Work

@dataset{Mobiusi2026,
  title={5G-Advanced RAN KPI Root Cause Diagnosis Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/4f85b294b7fe9360ce2ff3a56ba69b82?dataset_task_cate_id=6},
  urldate={2026-10-22},
  keywords={5G-Advanced dataset,RAN root cause diagnosis,KPI anomaly analysis,radio network operations,network alarm analysis,configuration change diagnosis,abductive reasoning data,reasoning SFT},
  version={1.0}
}

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