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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".