Characterization and modeling of erbium-doped fiber amplifiers and impact of fiber dispersion on semiconductor laser noise
Bibliographic record
Abstract
This thesis describes theoretical and experimental studies on two subjects: first is characterization, design and modeling of erbium-doped fiber amplifiers (EDFAs); second is the effect of fiber dispersion on the noise of distributed feedback (DFB) lasers and the impact of this effect on the performance of 1550nm video lightwave transmission systems. A simple electrical measurement technique for EDFA noise figure characterization is developed which has significantly better accuracy than other methods reported. This is achieved by noise measurements at identical detected optical power levels, with and without EDFA present. This approach ensures that the system noise level is identical in both measurements, thereby even small EDFA noise levels can be separated accurately from the large noise of the measurement system by subtracting the two noise measurements. Using this technique an excellent agreement is obtained between optically- and electrically-measured noise figures of saturated EDFAs. This result is in contrast to earlier reports by Willems and van der Platts from Bell Laboratories, showing significant discrepancies between optically- and electrically-measured noise figures of a saturated EDFA which sparked a serious controversy over the appropriate approach to model and measure the noise figure of EDFAs. Using the general, radially dependent rate-equation EDFA model, it is shown that highest-efficiency operation of saturated EDFAs is achieved with erbium distributed throughout the entire fiber core, in contrast to generally-accepted design principles. A simplified one-dimensional steady-state model for gain and noise in such EDFAs is derived which is accurate for any arbitrary distribution of erbium doping inside the fiber core. It is shown that the saturation parameters normally included in conventional models can be eliminated without loss of accuracy, with the resulting model requiring only small-signal gain and loss coefficients as parameters. This great simplification eases fiber characterization, and enhances accuracy in predicting amplifier performance. DFB laser relative intensity noise (RIN) variation induced by fiber chromatic dispersion is measured in the range of frequencies relevant to cable television systems. For two analog lasers tested, RFN degradation as large as 15dB is observed after 48km of standard fiber at a baseband frequency of 800MHz. The degradation increases with frequency, affecting higher channels the most. The experimental results are in excellent agreement with a simple theory by Yamamoto, which only requires knowledge of the laser linewidth to determine the RIN degradation. It is shown that this RIN degradation can significantly impair system carrier-to-noise ratio.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".