Estimating the Outage Probability Due to Polarization Dependent Loss Using Threshold Exceedances
Bibliographic record
Abstract
The method of threshold exceedances is used to reliably estimate the outage probability due to per-span polarization dependent loss (PDL). Bit-wise achievable information rate (BW-AIR) data for 10,000 instances of the link PDL are obtained using a simulation model that accurately captures the effect of PDL on both the signal and amplified spontaneous emission noise. The root mean square error between the mean excess function and a theoretical fit to it, and the average squared absolute error are compared for the critical step of determining the threshold above which transformed BW-AIR data are represented by the generalized Pareto distribution (GPD). Four techniques are considered for determining values of the two parameters that specify the GPD. The outage probability is defined in terms of the BW-AIR being less than a threshold value for the generalized mutual information (GMI), as determined by a specified value for the normalized GMI. To thoroughly demonstrate the approach, the dependence of the outage probability on the per-span PDL, the number of spans, and the constellation entropy is considered for 32 Gbaud, dual-polarization 64-ary quadrature amplitude modulation with uniform and probabilistically shaped constellations. The extent to which the outage probability can be reduced by decreasing the constellation entropy is quantified.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".