AI-Assisted Online Compensation of SRS Effects in OTDR Traces for Super C+L-Band Systems
Bibliographic record
Abstract
The emergence of wideband optical networks, particularly those utilizing super C+L-band transmission, introduces new challenges for in-service optical time-domain reflectometer (OTDR) due to the effects of stimulated Raman scattering (SRS). As OTDR probe pulses propagate through a fiber carrying high-bandwidth traffic, they experience wavelength-dependent amplification or depletion from SRS, resulting in distorted traces that no longer accurately reflect the fiber’s attenuation profile. In this work, we present a Transformer model-based online approach to correct OTDR trace distortions caused by super C+L-band traffic to restore the fiber loss profile. Notably, the model operates solely on the distorted OTDR trace without requiring knowledge of the fiber launch power distribution or any auxiliary input. The approach is experimentally validated over a 12 THz super C+L-band transmission system by varying the total fiber input power to induce different amount of SRS gain in the OTDR traces. By applying the correction method, the mean absolute errors (MAE) between the OTDR traces distorted by the traffic and the traces when there is no traffic were reduced from 4.7, 3.24, 2.18, and 1.51 dB to 0.072, 0.062, 0.063, and 0.09 dB, respectively, across four launch power settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".