Irregular rate-compatible LDPC codes for capacity-approaching hybrid-ARQ schemes
Bibliographic record
Abstract
In this paper, we describe the construction method of a family of irregular rate-compatible low-density parity-check (LDPC) codes by a combination of puncturing and extending techniques. In particular, we introduce a suitable structure for the extended parity-check matrices which preserves the structure of LDPC codes during extensions. Based on this construction, a family of efficient rate-compatible linear-time encodable codes are generated from an optimized irregular mother code of rate 8/13 and information block length k=1024. The rates of the codes vary from 8/10 to 8/19 and employing them in a type-II hybrid ARQ scheme results in a throughput which is only 0.7 dB away from the Shannon limit. This improves over the existing schemes, based on turbo codes and LDPC codes, by up to 0.5 dB.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".