Experimental Validation of Periodic Codes for PON Monitoring
Why this work is in the frame
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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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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) | 0.000 | 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