Fast and Efficient Dynamic WDM Semiconductor Optical Amplifier Model
Bibliographic record
Abstract
A novel state-variable model for semiconductor optical amplifiers (SOAs) that is amenable to block diagram implementation of wavelength division multiplexed (WDM) signals and fast execution times is presented. The novel model is called the reservoir model, in analogy with similar block-oriented models for Raman and erbium-doped fiber amplifiers (EDFAs). A procedure is proposed to extract the needed reservoir model parameters from the parameters of a detailed and accurate space-resolved SOA model due to Connelly, which was extended to cope with the time-resolved gain transient analysis. Several variations of the reservoir model are considered with increasing complexity, which allow the accurate inclusion of scattering losses and gain saturation induced by amplified spontaneous emission. It is shown that at comparable accuracy, the reservoir model can be 20 times faster than the Connelly model in single-channel operation; much more significant time savings are expected for WDM operation. The model neglects intraband SOA phenomena and is thus limited to modulation rates per channel not exceeding 10 Gb/s. The SOA reservoir model provides a unique tool with reasonably short computation times for a reliable analysis of gain transients in WDM optical networks with complex topologies
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".