Generalized Probability Density Function of Polarization-Dependent Loss in Optical Links
Bibliographic record
Abstract
In modern optical networks, reconfigurable optical add-drop multiplexers consisting of wavelength selective switches are widely adopted, and they are the major cause of polarization dependent loss (PDL). Link PDL evolves with time due to the time-varying polarization state of propagating light in the optical fiber, and this randomness needs to be considered in the design and operation of optical networks. Therefore, characterizing link PDL using statistical methods is important. It is well known that link PDL in ultra long-haul systems containing a large number of PDL elements is Maxwellian-distributed. However, it is not appropriate when links include a reduced number of PDL elements, or there exist a few dominant PDL elements. In this letter, statistics of link PDL is studied, and a generalized probability density function (PDF) is derived for links including a variety of number of elements. Simulations and experiments are performed, and results show that the generalized PDF fits in a wide scope of realistic scenarios. On the other hand, the conventional Maxwellian distribution exhibits significant discrepancy with the actual one in the studied cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".