Low-cost technique for gain and noise figure measurement of erbium-doped fiber amplifiers using a broadband source and filter tuning
Bibliographic record
Abstract
We report a unique low-cost technique for broadband gain and noise figure characterization of erbium-doped fiber amplifiers using an amplified-spontaneous-emission (ASE) source and a tunable filter with multiple steep slopes. The filter edge is tuned in steps of 10 pm and a series of output versus input power spectral density data points are taken at a fixed wavelength. Gain and noise figure of the amplifier are obtained by extracting the slope and intercept of output versus input power spectral density. The results obtained over a 20 dB total input power range are in good agreement (within ± 0.2 dB) with those obtained using conventional spectral-interpolation technique employing multiple DFB lasers at 100 GHz spacing over the C-band. The required filter depth is about 35 dB. Our method has several major advantages: (1) Low cost, as there is no need for multiple DFB laser sources or high-speed AOM modulators and RF drivers; (2) Immune to steady-state noise in the source; (3) Can be used to characterize amplifiers with fast dynamics as its accuracy is in principle not affected by the response time of the amplifying medium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".