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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".