Enhanced Regular Perturbation-Based Nonlinearity Compensation Technique for Optical Transmission Systems
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it