eNodeB Failure Detection from Aggregated Performance KPIs in Smart-city LTE Infrastructures
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Bibliographic record
Abstract
In this paper we show how Supervised Binary Classification techniques can be used to tackle the problem of eNodeB failure detection in an LTE network carrying Machine-to-Machine (M2M) smart-city traffic. 22 different classifiers are trained with data from two 24 hrs simulations with different levels of traffic volume. Input features for the classification models are built aggregating packet generation and access collisions from the eNodeB on which failures are being detected, as well as from its closest neighbors, by computing statistics for each time-bin. Given that network service providers generally process real-time data to produce periodic aggregated summaries, we explore the effect of different levels of granularity in data aggregation and their effect on our ability to detect failures. The M2M traffic data was gathered from a simulated LTE network that uses publicly available geographic databases from the city of Montreal. With Linear Support Vector Machines (L-SVMs) and Bagged Decision Trees (BDT), failure detection rates above 97.5 % were achieved, with false positive rates under 2.8 %, showing that, even with 30 minutes aggregations, it is feasible to extract meaningful failure information.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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