Application of preemphasis to achieve flat output OSNR in time-varying channels in cascaded EDFAs without equalization
Bibliographic record
Abstract
In this paper, we present a new method for optical signal-to-noise ratio (OSNR) equalization of wavelength division multiplexed (WDM) channels at the end of a cascade of several erbium-doped fiber amplifiers (EDFAs) by use of preemphasis, as well as the proper choice of EDFA design parameters. Identical OSNR at the end of the cascade ensures better signal detection and quality of service. The dynamics of the equalizing method have been demonstrated by simulation for single- and double-stage amplifier designs using a numerical model incorporating time variation effects in EDFA. Calculations are based on the solution of a transcendental equation describing the dynamics of the reservoir, i.e., the total number of excited ions, for each EDFA. Traffic on eight WDM channels is modeled as statistically independent ON-OFF time-slotted sources. In addition, 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 OSNR variation. We show that it is possible to achieve dynamic OSNR equalization for a WDM system by the use of preemphasis and an appropriate choice of EDFA parameters, without resorting to optical equalization filters. Most previous equalization methods are static with flat gain for a given inversion level in the amplifier. Changes in the input power (due to network reconfiguration or packetized traffic) will lead to a varying inversion level and hence non optimal equalization.
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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 | 0.000 |
| 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 | 0.000 |
| 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".