Implication of Parameter Values on Low-Pass Filter Assisted Digital Back Propagation for DP 16-QAM
Bibliographic record
Abstract
The low-pass filter (LPF) assisted digital back propagation (DBP) algorithm is investigated for 112 Gb/s dual-polarization 16-ary quadrature-amplitude-modulation transmission up to 2400 km. The performance implications of the parameter values for the LPF-DBP algorithm are considered in detail through determining the dependence of the bit error ratio (BER) on the number of nonlinear compensation (NLC) steps per span for optimum parameter values and the sensitivity of the BER to nonoptimum parameter values. The sensitivity to nonoptimum values is investigated as well for different transmission lengths and launch powers. It is shown that the optimum parameter values depend on the NLC step size, but that for a given step size representative values can be used for a range of transmission lengths and channel launch powers.
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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".