In-Band Power Ripple Detection and Localization Using Longitudinal Power Monitoring
Bibliographic record
Abstract
Optical performance monitoring has undergone a significant transformation with the advent of longitudinal power profile estimation (PPE), enabling advanced monitoring of signal and transmission link characteristics using only the received signal at the receiver side. As modern optical networks operate with higher symbol-rate signals, they require more extensive and accurate monitoring for effective management and optimized performance through just-enough margin systems. This paper presents the application of PPE for in-band power ripple (IPR) monitoring, demonstrating its effectiveness in detecting and localizing spectral variations. Two PPE-based techniques are introduced: sub-band PPE, which estimates spectral power within digital sub-bands to achieve higher granularity in signal power estimation, and reference-sweeping PPE, which adapts reference waveforms to match distortion patterns. While sub-band PPE allows precise detection of IPR with arbitrary shapes at the expense of spatial resolution, reference-sweeping PPE enhances efficiency by eliminating the need for deconvolution and reducing the number of estimation points when the IPR shape is approximated by a simple form. Simulation and experimental validation confirm the effectiveness of these approaches in high-baud-rate systems. This enables more accurate transmission quality assessment, facilitates rapid fault detection and localization, and further improves PPE performance, contributing to enhanced optical network reliability and efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".