Learned Signal-to-Noise Ratio Estimation in Optical Fiber Communication Links
Bibliographic record
Abstract
This paper proposes a signal-to-noise ratio (SNR) estimator based on recurrent neural network (RNN) in optical fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the received signal. The proposed input features do not require knowledge of the transmitted symbols. In the proposed SNR estimator, three different RNN models are investigated, namely simple RNN, gated recurrent units, and long short-term memory. The overall computational complexity of the three models of the proposed estimator, including the feature extraction and RNN structures, are analyzed. Numerical results show that the three models of the proposed estimator provide a trade-off between the complexity of the RNN structure and estimation accuracy. Furthermore, the proposed estimator achieves a better SNR estimation accuracy and reduces the overall computational complexity compared to the literature.
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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) | 0.000 | 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 | 0.001 | 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 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".