Lowering Error Floor of LDPC Codes Using a Joint Row-Column Decoding Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Low-density parity-check codes using the belief-propagation decoding algorithm tend to exhibit a high error floor in the bit error rate curves, when some problematic graphical structures, such as the so-called trapping sets, exist in the corresponding Tanner graph. This paper presents a joint row-column decoding algorithm to lower the error floor, in which the column processing is combined with the processing of each row. By gradually updating the pseudo-posterior probabilities of all bit nodes, the proposed algorithm minimizes the propagation of erroneous information from trapping sets into the whole graph. The simulation indicates that the proposed joint decoding algorithm improves the performance in the waterfall region and lowers the error floor. Implementation results into field programmable gate array (FPGA) devices indicate that the proposed joint decoder increases the decoding speed by a factor of eight, compared to the traditional decoder.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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