Effectiveness of gain control in EDFAs against traffic with different levels of bursty behaviour
Bibliographic record
Abstract
Previous publications have addressed the impact of bursty, self-similar traffic on erbium-doped fibre amplifiers (EDFA). Several gain control techniques have been suggested to combat the gain fluctuations of EDFA with this type of traffic. The effectiveness of two such methods are investigated, namely highly inverted amplifiers and all-optical gain clamping, while varying the parameters characterising the burstiness of the sources. While previous publications have focused on the effect of the average load or activity factor, the paper further investigate the dependence on the relative variability of the packet burst lengths (ON times), and lengths of the interburst idle times (OFF times). Both gain control methods reduce the output power and signal-to-noise ratio excursions with respect to a standard amplifier chain. The authors find that, for an eight channel WDM system and a cascade of six EDFAs, all-optical gain clamping can reduce the variations by a factor of five, provided adequate power is present in the clamping laser, while highly inverted amplifiers have variations reduced by a factor of two. The authors find that, in a clamped chain, the source activity factor does not uniquely determine the required level of lasing power. The authors also deduce that information on the relative variability of ON and OFF times is essential.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".