Combined PMD-PDL effects on BERs in simplified optical systems: an analytical approach
Bibliographic record
Abstract
A new analytical evaluation of polarization-induced error probability for optical systems consisting of both PMD and PDL is presented. Using a simplified model containing a lumped PMD-PDL fiber, an amplifier with ASE noise, an idealized optical filter, and an electrical filter with integrate-and-dump response, a closed-form of the probability density of the filtered current is obtained. This allows us to evaluate the BER affected by the PMD and PDL. Based on this, two polarization related effects, i.e., the PMD and PDL directional coupling and the polarization-induced intersymbol interference (ISI), are studied. We show that the PMD and PDL directional coupling can be strongest when the PMD vector perpendicularly correlates with both the PDL vector and the input signal polarization in the 3D Stokes space. Besides, its impact on bit-error-rate (BER) strongly depends on the PDL value. We also find that, for an optical system with realistic parameters, the impact of polarization-induced ISI on the BER is mostly caused by the two closest neighbors of the desired bit. Related with these two polarization effects is the PMD value fluctuation. Large PMD value variation can play an overwhelming role in impairing the optical 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".