eNodeB Failure Detection from Aggregated Performance KPIs in Smart-city LTE Infrastructures
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".