Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks
Why this work is in the frame
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Bibliographic record
Abstract
Today's network operators strive to create self-healing cellular networks that have a fully automated troubleshooting management process. To this end, the network monitoring system should be capable of detecting issues, diagnosing them, and triggering the adequate recovery action. In this paper, we propose an unsupervised solution to diagnose the root causes of network issues. As monitoring systems collect a large number of logs from the different devices in their networks, it is possible to determine which connections resulted in a poor user experience and apply a failed/successful label. Our solution, Automatic Root Cause Diagnosis (ARCD), analyzes labeled connection logs to identify the major contributors to the network inefficiency (e.g., a faulty core device) as well as the incompatibilities between different elements (e.g., make and model of a phone not being able to access a service). We evaluate the effectiveness of our solution by using logs from three different real cellular networks. In each case, ARCD was able to identify the major contributors and the most widespread incompatibilities. In the three cases, the precision (detection accuracy) and the recall (detection rate) are higher than 90%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it