Noise Transfer Characteristics in a Semiconductor Optical Amplifier With Application to Wavelength Conversion Based on a Delay Interferometer
Bibliographic record
Abstract
A theoretical model is presented to analyze the noise transfer characteristics in a semiconductor optical amplifier (SOA) under the excitation of a noisy pump signal and a noise-free probe signal. An analytical expression is derived for the optical signal-to-noise ratio (OSNR) of the output probe signal from the SOA. The influence of the gain saturation of the SOA, and the pump and probe signal powers on the noise transfer characteristics is investigated. The noise transfer model is used to determine the output noise power of a delay interference wavelength converter. An analytical expression is obtained for the nonlinear phase change in the SOA, which determines the output power of the wavelength-converted signal. These results show that the noise transfer in the wavelength conversion can be suppressed by increasing the probe signal power, but that the improvement in the output signal OSNR relative to the input signal OSNR is accompanied by a reduction in the conversion efficiency. This fundamental tradeoff can be readily investigated during the design optimization process using the concise results derived in this paper.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".