A Non-Data-Aided OSNR Estimation Algorithm for Coherent Optical Fiber Communication Systems Employing Multilevel Constellations
Bibliographic record
Abstract
The performance of existing moments-based non-data-aided (NDA) optical signal-to-noise ratio (OSNR) estimation approaches degrades greatly for coherent optical systems employing multilevel constellations. We propose a novel NDA OSNR estimation algorithm, which provides enhanced performance for such systems. The proposed algorithm utilizes the empirical cumulative distribution function of the signal's amplitude to extract the information on the noise variance. Analytical and extensive simulation results show the feasibility and advantages of the algorithm. For the studied systems employing multilevel constellations such as 8-quadrature amplitude modulation (QAM), 16-QAM, 32-QAM, and 64-QAM, the proposed algorithm attains the derived Cramér-Rao lower bound. Furthermore, it achieves a lower mean square error with significantly lower complexity when compared to the conventional moments-based NDA estimation approach. Moreover, the impact of fiber nonlinearity is investigated with a five-channel Nyquist wavelength division multiplexing system, and the proposed algorithm outperforms the moments-based counterpart.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".