An analytical model for dispersion-managed 160 Gb/s OTDM transmission systems
Bibliographic record
Abstract
As the next generation of ultra-fast optical transmission systems, the optical time division multiplexing (OTDM) systems with a bit rate up to 160 Gb/s are actively being researched. Several experimental ultra-fast OTDM transmission systems with all-optical 3R-regeneration have been reported. With the development of ultra-fast OTDM technologies, techniques for rigorously evaluating the system performance through the calculation of bit error ratio (BER) have become increasingly important. We report a novel analytical model for estimating the performance of a 160 Gb/s OTDM receiver. The BER of the OTDM system is evaluated based on the calculation of the noise probability density function using the moment generating function. The optical pulse broadening by the fiber dispersion is compensated through the dispersion-managed approach and both nonlinearity and dispersion of the fiber channel are taken into account. Noise (ASE, shot and thermal) and performance-impairing factors (intrachannel interactions and timing jitter) are included in the calculation of the BER. A variational analysis approach is used to solve the optical pulse evolution over a periodical, dispersion-managed, nonlinear fiber channel as this yields an analytical expression for the received optical pulses. The OTDM demultiplexer is modeled as an optical gate controlled by an optical or electrical clock signal and the cyclostationary characteristic of the ASE noise after passing the OTDM demultiplexer is considered. Also the timing jittering between the signal pulse and the gate window and its effects on the signal decision are taken into account. Based on the proposed model, calculated results for the performance of the 160 Gb/s OTDM transmission systems are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".