Performance of carrier phase recovery for electronically dispersion compensated coherent systems
Bibliographic record
Abstract
An analytical approach taking into account carrier phase estimation (CPE) is presented to predict performance of quadrature phase shift-keying (QPSK) systems using coherent detection. Using this approach, system performance is found as a function of symbol rate, local oscillator (LO) linewidth, chromatic dispersion (CD) and signal-to-noise ratio (SNR). A new expression is derived for the covariance matrix of the conditional probability density function (pdf) of the decision statistic. This pdf is used to find bit error rate (BER) semi-analytically. Our analytical derivation assumes perfect removal of data modulation which corresponds to an ideal decision feedback (DF) carrier recovery. The validity of the analytical pdf for predicting BER is verified for a wide range of system parameters of interest in long haul systems. In addition, our semi-analytical BER provides a lower bound for the Viterbi-Viterbi (VV) BER, while showing the analytical BER previously proposed in the literature shows an overly pessimistic prediction of VV BER performance. We show that inaccuracy in previous analysis stems from overly simple model for the CPE when compensating large accumulated dispersion electronically. Finally, we study extension of our results to quadrature amplitude modulation (QAM). Preliminary simulation results are promising but the accuracy of our semi-analytical approach for predicting BER should be investigated further.
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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) | 0.000 | 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".