Enhanced Regular Perturbation-Based Nonlinearity Compensation Technique for Optical Transmission Systems
Bibliographic record
Abstract
The regular perturbation (RP) series used to analytically approximate the solution of the nonlinear Schrodinger equation has a serious energy-divergence problem when truncated to the first order. The enhanced RP (ERP) method can improve the accuracy of the first-order RP approximation by solving the energy divergence problem. In this paper, we propose an ERP-based nonlinearity compensation technique, referred to as ERP-NLC, to compensate for the fiber nonlinearity in a polarization-division multiplexed dispersion unmanaged optical communication system. We also propose a modified perturbation-based NLC (PB-NLC) technique by simple phase-rotation (PR) of the nonlinear coefficient matrix, referred to as the PR-PB-NLC. The PR-PB-NLC can be considered as a by-product of the ERP-NLC technique. We show through numerical simulation that, for a 256 Gb/s single-channel system, the proposed ERP-NLC technique improves the Q-factor performance by ~1.2 dB and ~0.6 dB when compared to the electronic dispersion compensation (EDC) and the PB-NLC techniques, respectively, at a transmission distance of 2800 km. Also, the result for a 1.28 Tb/s wavelength-division multiplexed five-channel transmission system at the same transmission distance shows that the Q-factor performance of the ERP-NLC technique is improved by ~0.6 dB and ~0.4 dB when compared to the EDC and the PB-NLC techniques, respectively. The simulation results for the PR-PB-NLC technique for a single- or five-channel transmission system show an improved Q-factor performance when compared to the EDC and PB-NLC techniques. Finally, we show that the proposed performance enhancement comes with a negligible increase in the computational complexity for the ERP-NLC and PR-PB-NLC techniques when compared to the PB-NLC technique.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".