mmWave RAN Fault Root Cause Reasoning Dataset

#fault root cause analysis #logical reasoning #candidate cause ranking #evidence chain generation #mmWave network operations #base station troubleshooting #wireless network optimization #network quality monitoring

3620
0.3GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This dataset contains fault diagnosis question-and-answer cases for millimeter-wave radio access networks, with cross-layer evidence such as alarm records, performance metrics, configuration states, and field observations. It also provides candidate-cause comparisons, root cause assessments, evidence chains, and explanatory answers. The data supports training and evaluating models that reason from network evidence, rank possible causes, and produce grounded diagnostic conclusions for network operations assistance.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
confidence	number	The estimated confidence in the selected root cause, represented as a value from 0 to 1.
root_cause	string	The selected most likely root cause inferred from the available cross-layer evidence.
alarm_evidence	object	Raw alarm records, including alarm codes, severity, timestamps, affected cells, beams, or network elements.
evidence_chain	array	Ordered evidence statements connecting the raw observations to the selected root cause.
candidate_causes	array	The original list of possible fault causes to consider when answering the diagnostic question.
reasoning_answer	string	An explanatory answer describing the diagnostic logic, evidence interpretation, and why alternative causes are less likely.
field_observation	string	Original field notes describing symptoms, environmental conditions, recent changes, or troubleshooting observations.
configuration_state	object	Raw configuration values and status information for relevant cells, beams, carriers, handovers, and network functions.
diagnostic_question	string	The original question asking for a fault diagnosis or comparison of possible causes.
performance_metrics	object	Raw performance measurements relevant to the incident, such as throughput, BLER, latency, PRB utilization, or signal quality.
ranked_cause_assessments	array	A comparison of candidate causes, ranked by how well each is supported by the available evidence.

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?: Cases may include raw alarms, performance metrics, configuration states, field observations, diagnostic questions, and candidate causes, along with root cause assessments, cause rankings, evidence chains, and explanatory answers.

Which model capabilities can it support?: It supports training models to compare possible fault causes, integrate cross-layer evidence, infer likely root causes, and explain their conclusions. It can also be used to evaluate network fault diagnosis and logical reasoning.

How can the data be obtained, and what are the license terms?: The available acquisition method, permitted uses, and commercial use conditions are specified on the product page and in the applicable license agreement. Review the terms and data handling requirements before use.

Can it be used for production network troubleshooting?: It can support the development and evaluation of diagnostic assistance models. Model outputs should be verified against live network conditions, operational procedures, and expert judgment; dataset-based conclusions alone should not trigger production network changes.

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Cite this Work

@dataset{Mobiusi2026,
  title={mmWave RAN Fault Root Cause Reasoning Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/0efb0c7be5d8eaf10f0813cd73ec80f6},
  urldate={2026-10-26},
  keywords={mmWave RAN dataset,wireless network fault diagnosis,root cause analysis,cross-layer alarm reasoning,network operations QA,5G mmWave network,logical reasoning training data,wireless performance analysis},
  version={1.0}
}

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