Zero-overhead phase noise compensation via decision-directed phase equalizer for coherent optical OFDM
Bibliographic record
Abstract
We report and investigate the feasibility of zero-overhead laser phase noise compensation (PNC) for long-haul coherent optical orthogonal frequency division multiplexing (CO-OFDM) transmission systems, using the decision-directed phase equalizer (DDPE). DDPE updates the equalization parameters on a symbol-by-symbol basis after an initial decision making stage and retrieves an estimation of the phase noise value by extracting and averaging the phase drift of all OFDM sub-channels. Subsequently, a second equalization is performed by using the estimated phase noise value which is followed by a final decision making stage. We numerically compare the performance of DDPE and the CO-OFDM conventional equalizer (CE) for different laser linewidth values after transmission over 2000 km of uncompensated single-mode fiber (SMF) at 40 Gb/s and investigate the effect of fiber nonlinearity and amplified spontaneous emission (ASE) noise on the received signal quality. Furthermore, we analytically analyze the complexity of DDPE versus CE in terms of the number of required complex multiplications per bit.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".