Learning EPON delay models from data: a machine learning approach
Bibliographic record
Abstract
There have been a large number of studies focused on the characterization of the upstream delay in time-division multiplexing passive optical networks (TDM-PONs). However, most of them focus on finding equations for the average delay and ignore other useful metrics like delay percentiles, which are of paramount interest in dimensioning PONs with delay guarantees. This work shows how to learn delay models from data using supervised machine learning (ML) techniques. Essentially, a nonlinear regression ML algorithm is trained with PON simulation data, showing that it can provide accurate equations for such metrics of interest. In particular, we obtain an R 2 score above 80% under Poisson traffic and above 65% under self-similar traffic, and we provide a general equation for any delay percentile in the upstream channel of a PON employing interleaved polling with adaptive cycle time. We further show its applicability in dimensioning Tactile Internet and 5G transport support scenarios.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".