Investigating probabilistic constellation shaping for dual-polarization PAM8 signals at different data rates
Bibliographic record
Abstract
DP-PAM8 modulated signals with probabilistic constellation shaping (PCS) are investigated for ultrahigh-data rates with diverse shaping strengths and DGD values using direct detection for short distances mainly seen in data centers. The investigation is conducted using numerical simulation, where system performance improvement is achieved when PCS is used. The probabilistic shaping mitigated the uncompensated DGD and dispersion effects in the transmission system. We found that the high-powered symbols close the eye causing high symbol error. Applying PCS opens the eye of the highpowered symbols but closes the eye for low-powered ones. Thus, optimization of the strength of shaping is necessary to get the best performance. Experiments were conducted to investigate the effect of probabilistic shaping on PAM8 system amplified using an optical semiconductor amplifier (SOA). A single polarization PAM8 case was only demonstrated due to accessibility limitations of required parts for dual-polarization PAM8.
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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 | 0.002 |
| 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".