Optical Nonlinear-Phase-Noise Compensation for 9x32 Gbaud PolDM-16 QAM Transmission using a Code-Aided Expectation-Maximization Algorithm
Bibliographic record
Abstract
Nonlinearity-induced phase noise has become a major obstacle in long-haul coherent fiber-optic communication systems. Such phase noise has been shown to be signal-dependent and hence correlated over time. We propose a code-aided expectation-maximization algorithm to mitigate such nonlinear phase-noise, iteratively utilizing both the time correlation of the nonlinearity-induced impairments, and a soft-decision error-control code. Simulation and experimental results show that on a dual-polarization wavelength-division-multiplexed 16 QAM system, launch-power tolerance can be increased by 1.5 dB, and the optical signal-to-noise ratio requirement can be relaxed by 0.3 dB to achieve the same Q^2 -factor.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".