Experimental Validation of Periodic Codes for PON Monitoring
Bibliographic record
Abstract
In this paper we investigate both experimentally and via simulation the monitoring of fiber link quality in a PON using optical coding technology. We use a new, simple and cost-effective coding scheme well adapted to the monitoring application, namely periodic coding. We discuss design issues for periodic coding and the optimal detection criteria. We develop a reduced complexity maximum-likelihood sequence estimation (RC-MLSE) algorithm for monitoring. We conduct experiments to validate our detection algorithm using four periodic encoders that we designed and fabricated. These encoders were placed at roughly equal distances (within a meter) to represent a partial return from a very high density (geographically) PON. The measured data were fed into our detection algorithm and the exact location of each subscriber was correctly identified. Using the experimental data for the encoder's impulse responses, we completed Monte-Carlo simulations for more realistic PON geographical distributions with randomly located customers. Error-free detection is achieved. We also highlight the importance of averaging to remedy the power/loss budget limitations in our monitoring system to support higher network sizes.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".