Pilot-aided carrier phase recovery for M-QAM using superscalar parallelization based PLL
Bibliographic record
Abstract
In this paper, we present a carrier phase recovery (CPR) algorithm using a modified superscalar parallelization based phase locked loop (M-SSP-PLL) combined with a maximum-likelihood (ML) phase estimation. Compared to the original SSP-PLL, M-SSP-PLL + ML reduces the required buffer size using a novel superscalar structure. In addition, by removing the differential coding/decoding and employing ML phase recovery it also improves the performance. In simulation, we show that the laser linewidth tolerance of M-SSP-PLL + ML is comparable to blind phase search (BPS) algorithm, which is known to be one of the best CPR algorithms in terms of performance for arbitrary QAM formats. In 28 Gbaud QPSK (112 Gb/s) and 16-QAM (224 Gb/s), and 7 Gbaud 64-QAM (84 Gb/s) experiments, it is also demonstrated that M-SSP-PLL + ML can increase the transmission distance by at least 12% compared to BPS for each of them. Finally, the computational complexity is discussed and a significant reduction is shown for our algorithm with respect to BPS.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".