Cross-gain modulation effect on the behaviour of packetized cascaded EDFAs
Bibliographic record
Abstract
In this paper, we demonstrate the importance of the number of wavelength division multiplexing (WDM) channels and the network traffic variability on the dynamics of a cascade of erbium-doped fibre amplifiers (EDFAs) fed by packet traffic in burst mode. The dynamics are determined using a numerical model incorporating time variation effects in the EDFA. Calculations are based on the solution of a transcendental equation describing the time evolution of the reservoir , i.e. the total number of excited ions, for each EDFA. Traffic on WDM channels is modelled as statistically independent ON/OFF time-slotted sources. We find that the cross-gain modulation effect depends on the number of WDM channels and also on network traffic variability. As the number of WDM channels increases, and even though each one is highly variable in time, the dynamics of the total input power exhibits less fluctuation, allowing the reservoir to have a more suitable behaviour. In addition, the swings of the reservoir and the total output power decreases when we increase the network utilization factor. This positive effect is not felt on the output power excursion of an individual WDM channel. This is due to the fact that the cumulative effect of the reservoir fluctuations along a cascade will lead to further broadening of any individual channel power probability density function (PDF). Finally, we investigate the effect of gain clamping of the first amplifier in the cascade - by implementing a ring laser and propagating the lasing power through the cascade - on the statistics of several measurable entities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".