An Efficient Approach for Time-Domain Simulation of Pulse Propagation in Optical Fiber
Bibliographic record
Abstract
A novel approach is proposed for split-step time-domain simulation of pulse propagation in optical fiber. In this approach, a Fourier series expansion method is introduced for time-domain digital filter extraction from any given fiber transfer function. With such extracted filter coefficients and a double Tukey window function, the filter length can be optimized for a given error tolerance. This method is validated by comparing our simulation results with that obtained from the well-known split-step frequency-domain method. Through several simulation examples, we find that this solution technique is much more efficient than other existing time-domain approaches-as much as 92% of the computation time can be saved. It even outperforms the well-known split-step frequency-domain fast Fourier transform method in terms of the computation efficiency, under the condition that the input signal samples are huge-a situation we often meet in dealing with wavelength division multiplexing systems. Moreover, we find that the truncation effect at the computation window edge introduced by the time-domain algorithm is less severe than the aliasing effect associated with the frequency-domain method, not to mention that we can eliminate the truncation error by using a sliding window, only at a small cost on computation time.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".