5/10-Gb/s Burst-Mode Clock and Data Recovery Based on Semiblind Oversampling for PONs: Theoretical and Experimental
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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) <; 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-10</sup> and packet loss ratio (PLR) <; 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> while 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it