Frequency-Domain Volterra-Based Equalization Structures for Efficient Mitigation of Intrachannel Kerr Nonlinearities
Bibliographic record
Abstract
Toward reduced-complexity digital implementation, frequency domain Volterra-based nonlinear equalization (VNLE) structures for multistep fiber nonlinearity compensation are proposed. In the cascade structures, nonlinear equalization is performed before (cascade-1) or after (cascade-2) the dispersion compensation in each step. Superior performance with the shorter discrete Fourier transform (DFT) lengths and fewer equalization steps compared to the conventional VNLE with parallel structure is demonstrated in a transmission experiment. The experimental results are obtained for 256 Gb/s single-carrier dual-polarization 16-ary quadrature-amplitude-modulation with root-raised-cosine pulse shaping and a roll-off factor of 0.1. The new cascade structures demonstrate superior robustness to insufficient DFT lengths and/or a limited complexity budget. Compared to the conventional parallel arrangement of linear and nonlinear compensation filters, the cascade-1 structure provides more than 90% complexity reduction without any notable performance penalty. The structure enables the extension of the transmission reach by 1570 km, a 48% increase compared to the linear solution that uses only electronic dispersion compensation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".