Coded-Aided Phase Tracking for Coherent Fiber Channels
Bibliographic record
Abstract
In coherent optical systems laser phase noise can interact with digital equalization to cause equalization-enhanced impairments, which are a major obstacle for applying higher order modulation formats in coherent optical systems with digital chromatic dispersion compensation. In this paper a code-aided expectation maximization method to track phase noise in such systems is presented. A common measure of laser phase noise is the linewidth. It is shown that with ~ 11% redundancy, the laser linewidth tolerance for 975 km transmission distance can be increased by 50%, or the system reach for a laser linewidth of 5 MHz can be doubled. A phase-noise-robust 16-point 4-4-4-4 ring constellation was found to have better performance compared to 16QAM and a 2-6-8 ring constellations. Performance can be further improved with a lower code rate and fewer pilot symbols. It is also shown that algorithmic complexity can be reduced without significant reduction in the performance by reducing iterations and using low complexity codes.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".