Online Correction of Distorted OTDR Traces Caused by Stimulated Raman Scattering in Wideband Optical Networks
Bibliographic record
Abstract
The recent interest in the development of wideband optical networks brings up challenges for in-service optical time-domain reflectometer (OTDR) operation due to the stimulated Raman scattering (SRS) effect. Depending on its wavelength, OTDR probe pulses can be pumped or depleted by wideband traffic signal during the propagation along a fiber span. Consequently, the measured traces are distorted and unable to provide accurate fiber loss profiles. In order to correct distorted traces in a real-time online manner, we propose a dither-based method that doesn't require any prior information on channel loading condition or fiber parameters. The proposed method prompts a low frequency sinusoidal power dither on the traffic light, and a large number of OTDR traces are collected in the positive and negative half cycles, respectively. Averaging traces from the two half cycles generates two differential traces, and SRS gain can be calculated and used for correction. The induced dither causes power variation of traffic signal, but this impact can be minimized substantially by employing a second dither at the end of fiber span properly. The proposed method is verified experimentally in a 700 km multiple spans fiber link. In the presence of SRS effect, the mean of absolute errors between the distorted traces and the actual loss trace are about 0.72 dB and 0.3 dB when OTDR pulses and traffic co-propagate and counter-propagate, respectively. By applying the proposed method, these errors are reduced to around 0.07 dB and 0.05 dB correspondingly. Meanwhile, performance of data transmission in terms of bit error rate is monitored during the process, and no penalty is observed.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".