Performances of Erbium-Doped Fiber Amplifier at 980-nm Pump Power in Various Parameter Combinations
Bibliographic record
Abstract
Optical fiber amplifiers are dominating the loss compensation in fiber optic communications systems, especially the Erbium doped types for communication spectrum based on silica fibers. The gain of the EDFA can be maximized by using appropriate design parameters and appropriate value of the material properties. This paper describes the derivation of the equations that govern the pump power, input signal propagation, gain and optimum fiber length of the amplifier. By using these deduced expressions, new tools have been developed using C programming and MATLAB and then the performances of EDFA pumped with 980-nm signal have been analyzed. The analysis results that the gain of the amplifier increases with the length of the amplifier, and also with the pump signal power. At higher values of signal power the gain becomes saturated. The gain has a nonlinear relation with the amplifier length for a particular pump power. From the experiments, it has been found that for a particular length the saturation gain increases with the carrier density of the amplifier, but at longer length the gain is almost independent of the carrier density. It has also been found that the pump threshold power increases linearly with the length and carrier density of the amplifier. The tools developed in the analysis process could be used by researchers in future for further investigation of the performance of EDFA in various parameter sets or, for finding a parameter set for specific target performance of the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".