Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine
Bibliographic record
Abstract
By adopting the cumulative distribution function of the received signal's amplitude as feature, a support vector machine-based algorithm is proposed to jointly classify the modulation format and estimate the optical signal-to-noise ratio (OSNR) in coherent optical communication systems. Three commonly-used quadrature-amplitude modulation (QAM) formats are considered. Numerical simulations have been carried out in the OSNR ranges from 5 to 30 dB, and results show that the proposed algorithm achieves a very good modulation classification (MC) performance, as well as high OSNR estimation accuracy with a maximum estimation error of 0.8 dB. Optical back-to-back experiments are also conducted in OSNR ranges of interest. A 99% average correct MC rate is observed, and mean OSNR estimation errors of 0.38, 0.68, and 0.62 dB are noticed for 4-QAM, 16-QAM, and 64-QAM, respectively. Furthermore, compared with the neural networks-based joint estimation algorithm, the proposed algorithm attains better performance with comparable complexity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".