Equalization-Enhanced Phase Noise Compensation in Coherent Fiber Receivers
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".