Statistical BER Analysis of Wireline Links With Non-Binary Linear Block Codes Subject to DFE Error Propagation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a statistical model to accurately estimate post-FEC BER for high-speed wireline links using standard linear block codes, such as the RS(544,514,15) KP4 and RS(528,514,7) KR4 codes. A hierarchical approach is adopted to analyze the propagation of PAM-symbol and FEC-symbol errors through a two-layer Markov model. A series of techniques including state aggregation, time aggregation, state reduction, and dynamic programming are introduced making the time complexity to compute post-FEC BER below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-15</sup> reasonable. Error bounds associated with each method are found. The efficiency of the proposed model allows it to handle a larger state space, more DFE taps, and more sophisticated linear block codes than prior work. A 4-PAM 60 Gb/s wireline transceiver fabricated in a 7 nm FinFET technology is used as a test vehicle to validate this model. Measured data with two different channels reveals that the statistical model can properly predict the post-FEC error floor with standard FEC codes. While this paper demonstrates the method for capturing DFE error propagation, the method is general and can be applied to model other communication systems having memory effects. Moreover, our proposed model can be easily extended to higher-level PAM schemes and other advanced equalizer architectures to assist in making architectural choices for wireline transceivers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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