A Rate-Compatible Puncturing Scheme for Finite-Length LDPC Codes
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Bibliographic record
Abstract
In this paper, we propose a rate-compatible puncturing scheme for finite-length low-density parity-check (LDPC) codes over the additive white Gaussian noise (AWGN) channel. The proposed method is applicable to any LDPC mother code, both regular and irregular, and constructs punctured codes which perform well in both the waterfall and the error floor regions for a wide range of code rates. The scheme selects code bits to be punctured one at a time and based on a sequence of criteria. An important selection criterion is the number of short cycles with low approximate cycle extrinsic message degree (ACE) in which a candidate bit node participates. Simulation results demonstrate that the ACE measure, which is most often the determining criterion in the final selection of the puncturing candidates, plays an important role in improving the performance of the codes in both the waterfall and the error-floor regions. These results also demonstrate that the proposed scheme is superior to the existing puncturing methods, particularly when a wide range of code rates is desirable.
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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 | 0.001 | 0.000 |
| 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 | 0.003 | 0.001 |
| 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