Accurate BER evaluation for lumped DPSK and OOK systems with PMD and PDL
Bibliographic record
Abstract
New forms using Dirac bra-ket notations and their transformations to express electrically filtered currents are presented for optical systems using either binary differential phase-shift keying (2-DPSK) or ON-OFF keying (OOK) with lumped first-order PMD and PDL, arbitrary optical and electrical filtering and pulse shaping. Based on these forms, the moment generating functions (MGFs) and bit-error-ratios (BERs) for different systems are obtained. Our results show that, for a given BER, 2-DPSK requires ~ 5dB lower input signal-to-noise ratio than OOK. By comparing BERs for different polarization systems, we also show that the PDL-induced partially polarized noise can significantly improve system performance and reduce BER variation caused by the random couplings between signal polarization, PDL and PMD vectors.
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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 | 0.000 | 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) | 0.000 | 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".