EGN-Based Optimization of the APSK Constellations for the Non-Linear Fiber Channel Based on the Symbol-Wise Mutual Information
Bibliographic record
Abstract
We proposean optimization scheme to maximize symbol-wise mutual information (MI) of the amplitude-phase shift keying (APSK) constellations. We optimize APSK constellations for the additive white Gaussian noise (AWGN) channel and non-linear fiber channel. For the fiber channel, the optimization is based on the enhanced Gaussian noise (EGN) model and is performed at the maximum modified signal-to-noise ratio (SNR) of the optical system. By doing so, our optimization algorithm maximizes the MI rate while the impacts of shaping on the non-linear interference noise (NLIN) power are considered. Our results show that by optimizing APSK constellations specifically for the fiber channel, significant reach improvements can be achieved. In addition, by using the mutual information formula and assuming a model for the radius of the APSK rings, we obtain an equation that provides the optimal APSK radii based on that model. After demonstrating the accuracy of this model, we compare the optimized APSKs of the AWGN and fiber channel and show that in the AWGN channels, the optimal radius of the rings grows with the ring number faster than the radii of the APSKs optimized for the fiber channel. Our results indicate that in the highly non-linear regimes of the fiber channel, geometric shaping has to be performed for the fiber since AWGN-based shaping gives poor performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".