Joint Fiber Nonlinearity Mitigation and Compensation for Digital Sub-Carrier Multiplexing System
Bibliographic record
Abstract
Fiber nonlinearity is the bottleneck of optical communication systems and is commonly addressed by applying various nonlinearity mitigation and compensation techniques. In general, nonlinearity mitigation techniques offer modest improvements with minimal computational complexity, while nonlinearity compensation techniques provide significant performance gains at the expense of higher computational complexity. This motivates us to propose a joint nonlinearity mitigation and compensation approach in which the nonlinear effects during signal propagation are reduced to compensate for the residual nonlinearity at a lower complexity. Specifically, in this paper, we study the combination of symbol rate optimization (SRO) and perturbation-based nonlinearity compensation (PB-NLC) for a pre-chromatic dispersion compensated (pre-CDC) transmission of polarization multiplexing, digital sub-carrier multiplexing, and wavelength division multiplexing (PM-DSCM-WDM) optical communication system. We highlight the interplay between SRO and PB-NLC and demonstrate that joint SRO and PB-NLC provides considerable performance gain, significant complexity reduction, and an additional degree of freedom to balance performance-complexity trade-offs when compared to applying only PB-NLC in a conventional PM-WDM system. We observe that the pre-CDC transmission manifests a unique property that enables the distribution of PB-NLC computational complexity between transmitter and receiver. Leveraging the distinctive property, we propose a split PB-NLC technique for the PM-DSCM-WDM system. This technique combines the benefits of both pre-PB-NLC and post-PB-NLC, resulting in a modest performance improvement while maintaining the same computational complexity as post-PB-NLC.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".