Robust Synchronization Schemes for Preamble Detection in Burst-Mode Coherent Optical Networks
Bibliographic record
Abstract
A novel burst-mode frame detection scheme and DSP processing for 100 Gbps coherent time and frequency division multiplexed (TFDM) coherent PON networks has recently been demonstrated. Although many frame detection methods are available for burst-mode coherent PON networks, reliability of these schemes are rarely quantified. In this article, we present a burst-mode frame detection scheme, as continuation of our previous work, and extensively analyze its robustness through numerical, simulative and experimental analysis. Our proposed scheme has a high frequency offset (FO) tolerance of ∼1 GHz for 4GBaud or ∼6.25GHz for 25GBaud systems. At an SNR around soft decision (SD) FEC threshold with BER = 2.1e-2, our proposed scheme provides reliable frame detection with false frame detection probability as low as ∼1e-11 and ∼1e-17, corresponding to a false frame detection every ∼6 days and ∼15K years with a burst length of ∼20K symbols for 4GBaud DP-QPSK and DP-16QAM systems, respectively.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".