5/10-Gb/s Burst-Mode Clock and Data Recovery Based on Semiblind Oversampling for PONs: Theoretical and Experimental
Bibliographic record
Abstract
In this paper, we demonstrate a 5/10-Gb/s burst-mode clock and data recovery circuit (BM-CDR) for passive optical network (PON) applications. The BM-CDR is based on a phase-tracking oversampling (semiblind) CDR circuit operated at twice the bit rate and a clock phase aligner that makes use of a simple phase-picking algorithm for automatic clock phase acquisition. The design provides low latency and fast response without requiring a reset signal from the network layer. We experimentally test the proposed BM-CDR in a 20-km PON uplink. The BMCDR achieves a bit error rate (BER)-10and packet loss ratio (PLR)-6while featuring: 1) instantaneous (0 preamble bit) phase acquisition for any phase step (±27π rad) between successive bursts; 2) BER and PLR sensitivities of -24.2 and -25.4 dBm, respectively; 3) negligible burst-mode sensitivity penalty of 0.8 dB; 4) frequency acquisition range of 242 MHz; 5) consecutive identical digit (CID) immunity of 3100 bits; and 6) dynamic range of 3 dB. With the instantaneous phase acquisition, we predict the physical efficiency of the upstream PON traffic to be 99%. We also present a unified probabilistic theory for conventional CDRs, N times oversampling CDRs in either time or space, and BM-CDRs built from oversampling CDRs. This theory can quantitatively explain the performance of these circuits in terms of the BER and PLR. The theoretical model accounts for the following parameters: 1) silence period, including phase step and CIDs, between consecutive packets; 2) finite frequency offset between the sampling clock and data rate; 3) preamble length; 4) jitter on the sampling clock; and 5) pattern correlator error resistance. On the basis of this theory, we perform a comprehensive theoretical analysis to assess the tradeoffs between these parameters, and compare the results experimentally to validate the theoretical model.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".