Enhanced Signal-to-Noise Ratio Estimation in Optical Fiber Communications: A Pilot-Based Approach
Bibliographic record
Abstract
This paper presents two innovative, pilot-assisted, neural network (NN)-based signal-to-noise ratio (SNR) estimators for application in optical fiber communications. These estimators, termed pilot-assisted feature complexity reduction (PAF-CR) and pilot-assisted feature accuracy enhancement (PAF-AE), are designed to jointly estimate both linear and non-linear SNR components. The architectures of these proposed estimators employ feedforward NNs (FFNNs) for the SNR estimation, with PAF-CR utilizing a two-hidden layer FFNN and PAF-AE employing a single-hidden layer FFNN. Novel features are extracted from pilot signals to utilize the pilot overhead in transmitted signals, such as mean absolute error and mean signed deviation, which statistically measure the error between transmitted and received pilot signals. Additionally, features are directly extracted from the received data signal, such as average absolute deviation, entropy, and arithmetic mean, to capture its statistical dispersion characteristics. The proposed features are carefully selected to effectively capture the characteristics of both linear and non-linear SNR components. The estimation accuracy of the SNR components achieved by the proposed estimators is evaluated using the normalized root mean square error and the standard deviation of the estimation errors. A comprehensive computational complexity analysis of the proposed PAF-CR and PAF-AE estimators is conducted, expressed in terms of real-valued multiplications and additions. Numerical results illustrate that the proposed PAF-CR and PAF-AE estimators achieve a favorable trade-off between the SNR estimation accuracy and computational complexity compared with existing literature estimators. The proposed PAF-CR offers significant computational complexity reduction with a slight enhancement in estimation accuracy, while the proposed PAF-AE provides substantial estimation accuracy improvement while slightly decreasing computational complexity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.001 |
| 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".