Burst-mode clock and data recovery with FEC and fast phase acquisition for burst-error correction in GP0Ns
Bibliographic record
Abstract
We demonstrate experimentally for the first time the impact of forward error correction (FEC) on the performance of 622/1244 Mb/s burst-mode clock and data recovery (BM-CDR) with instantaneous phase acquisition (0 bit) for any phase step (plusmn27pi rads) for gigabit-capable passive optical network (GPON) optical line terminator (OLT) applications with (255, 239) Reed-Solomon (R-S) codes. Our design is based on commercially available SONET CDRs operated in 2times over sampling mode. This burst-mode receiver (BM-RX) provides a ~5 dB coding gain at bit error ratio (BER) of 10-10. We also show that this novel technique of employing FEC on BM-CDRs with fast phase acquisition time, provides a solution for fast burst-error correction giving reliable and predictable BERs in bursty-channels. The BM-RX meets the GPON physical media dependent layer specifications defined in the ITU-T G.984.2 recommendation. The coding gain can be used to increase the optical link budget as specified in the ITU-T G.984.3 standard, that is, support higher bit rates, achieve longer physical reach between the OLT and the optical network units (ONUs), as well as increase the number of splits per single PON tree.
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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 | 0.002 |
| 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 | 0.000 |
| 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".