Effect of modulator chirp and sinusoidal group delay ripple on the performance of systems using dispersion compensating gratings
Bibliographic record
Abstract
The implications that the nonideal characteristics of a dispersion compensating grating have on system performance are determined, for the most part, by the group delay ripple (GDR) of the grating response over the modulated signal bandwidth. Since the GDR typically exhibits an irregular variation with wavelength that has periodic features, it is convenient to use a sinusoidal variation to assess the implications on system performance. The portion of the grating bandwidth occupied by the modulated optical signal is determined by the carrier signal wavelength, bit rate, modulation format, and modulator chirp. The implications of modulator chirp on the performance of 10-Gb/s dispersion compensated systems are considered. Using a LiNbO/sub 3/ Mach-Zehnder modulator, an electroabsorption modulator, and a multiple quantum-well Mach-Zehnder modulator with distinct chirp properties, the results demonstrate that to accurately assess the implications of GDR, the properties of the modulator chirp must be considered. In particular, results for chirp-free optical signals underestimate the implications of the GDR on system performance.
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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 | 0.003 |
| 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".