PPE-Assisted Silent Failure Awareness in Optical Transport Networks
Bibliographic record
Abstract
The detection of silent failures in optical networks is inherently difficult, and their presence may jeopardize network reliability and operational efficiency, particularly as these networks scale to meet increasing bandwidth demands. Among the most elusive silent failure types are forwarding errors in wavelength selective switches (WSSs), which may misroute optical signals without triggering alarms, leading to prolonged service interruptions. In this work, we propose a novel failure detection and localization framework that leverages the distributed chromatic dispersion (CD) profile of the signal path using longitudinal power profile estimation (PPE) technique. PPE enables span-by-span reconstruction of the lightpath’s optical characteristics with resolution of a few tens of ps/nm for typical coherent signals, allowing for precise estimation of CD accumulation at each segment. By comparing the measured distributed CD profile against a topology-aware digital twin of the network, discrepancies caused by forwarding errors at specific WSS nodes are unambiguously identified and localized. Our method bypasses the limitation of total CD estimation and enhances fault localization resolution as validated by the experiment. To the best of our knowledge, this is the first experimental demonstration of silent failure identification and localization in optical networks based on PPE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".