Equalization-Enhanced Phase Noise Compensation in Coherent Fiber Receivers
Why this work is in the frame
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Bibliographic record
Abstract
Dispersion uncompensated fiber links are widely used due to their nonlinear benefits. In such links, dispersion requires electronic compensation after down-conversion by the receiver laser. Therefore, laser phase noise inevitably affects the received signal. While, in an optimal receiver, the phase noise should be compensated before dispersion, practical phase estimation is only feasible when dispersion is already compensated. Unfortunately, compensating receiver phase noise after dispersion compensation gives rise to equalization-enhanced phase noise (EEPN), which limits the system's performance, especially for high data rates over long system reaches. In this paper, we demonstrate that EEPN can be mitigated through signal processing. We derive the compensation expression and propose two different compensators depending on the availability of the receiver phase noise. Our study demonstrates that by using a simple time-variant finite impulse response filter, one can effectively compensate for EEPN. The simulation study validates these theoretical findings, revealing improved system performance, including enhanced system reach, optimal launch power, and reduced bit error rate compared to existing EEPN-controlled methods. Importantly, we show that the complexity of our compensators is comparable to existing methods, demonstrating its feasibility for practical implementation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it