Reinforcement Learning for Compensating Power Excursions in Amplified WDM Systems
Bibliographic record
Abstract
Wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) is a challenging issue in optical networks. We investigate a launch channel power control method using reinforcement learning (RL) to mitigate the power excursions of EDFA systems. A machine learning engine is developed, trained and evaluated with four different policy-gradient RL algorithms that are compared according to two main criteria: achieved power excursion reduction and learning time. Different scenarios are considered with 12-, 24-, 40- active channels at fixed wavelengths and with variable number of active channels (between 12 and 64) assigned randomly at different wavelengths during RL process. We show 62% power excursion reduction in the 40-channel scenario and 28% in the variable scenario, which demonstrates the promising role of online RL approach for controlling power excursion in EDFA systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".