Minimizing Fiber’s Nonlinear Interference Noise by Designing Launched Signal PSD
Bibliographic record
Abstract
According to the Gaussian noise (GN) model, nonlinear interference noise (NLIN) in fiber depends on the signal power spectral density (PSD). Consequently, optimizing the PSD of the pulse that modulates data, as the main factor influencing the PSD of the launched signal into the fiber, can effectively minimize fiber NLIN. In this study, we first employ the calculus of variations to identify the optimal band-limited pulse PSD that minimizes fiber NLIN. Next, we add other communication requirements, such as zero inter-symbol interference (ISI) and fast decay over time, as constraints to our design problem. For this case, we develop a general pulse model and formulate the design problem as an optimization problem. By solving this optimization problem, we find the optimal pulse PSD that not only minimizes NLIN power in fiber but also meets practical requirements. We study the time-domain impact of the designed modulating pulse PSD on the launched signal properties to gain insights into the nonlinearity benefits we achieve. We further analytically demonstrate that our designed pulse has favorable properties for the Godard timing recovery method. Through extensive simulations using the split-step Fourier method on a fiber with typical parameters and considering practical transmitter/receiver limitations, we illustrate the superior system reach and achievable data rate of our optimized pulses compared to existing pulse shapes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".