Deep Neural Network for Joint Nonlinearity Compensation and Polarization Tracking in the Presence of PDL
Bibliographic record
Abstract
This paper presents a novel deep linear and nonlinear compensation network (DLNCN) that effectively addresses linear and nonlinear distortion in conjunction with polarization-dependent loss (PDL), while also accounting for changes caused by the joint impact of PDL and time-varying rotation of the state of polarization (RSOP). To accomplish this, we introduce neural network layers dedicated for PDL compensation, and we devise a transfer learning approach that selectively updates weights in layers affected by the variations while keeping the remaining weights unchanged. To monitor RSOP with PDL, we employ a pilot-based acquisition and a pilot-aided decision-directed tracking technique. Our numerical tests demonstrate successful RSOP tracking in the presence of PDL impairments, outperforming state-of-the-art schemes by an average of over 0.75 dB in Q-factor for a dual-polarized 960 km 32 Gbaud 64-QAM transmission with a polarization linewidth of 3 kHz. These results highlight the effectiveness of our proposed deep neural network structure, which includes a dedicated layer for PDL compensation, and its ability to work seamlessly with RSOP tracking.
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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) | 0.001 | 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 | 0.001 |
| 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".