A dynamic multi-wavelength simulink model for EDFA
Bibliographic record
Abstract
The erbium doped fiber amplifier (EDFA) is an imperative element in DWDM networks. This all-optical amplifier enables simultaneous amplification of multiple wavelengths. Nevertheless, uneven gain across the usable wavelength region remains a concern. Understanding and modelling the dynamic characteristics of an EDFA is an important step towards achieving a flat gain spectrum. A Simulink model for investigating EDFA dynamics has been developed by Novak and Gieske (2002). Following their work, we have developed an enhanced version of the EDFA Simulink model with more capabilities. Previous results were verified for the given doped fiber length without noise. In addition, the forward amplified spontaneous emission (ASE) noise is added to the model and the optimum length is determined (with and without considering ASE) by simulation and used in all subsequent stages. A significant addition to the model is the ability to handle multiple channels using the absorption and emission coefficients previously obtained experimentally. The resulting model accurately represents EDFA gain dynamics and forward ASE. Simulation results show a 12 dB gain fluctuation across a 40 mn window per EDFA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".