Good LDPC codes over GF(q) for bandwidth efficient transmission
Bibliographic record
Abstract
In this paper, we combine irregular LDPC codes over GF(q) and q-ary modulations for bandwidth efficient transmission over AWGN channel and consider the optimization of the codes. To find a good LDPC code over GF(q), based on the previous work by Davey (1998, 1999), one may choose a row profile and minimize a nonlinear objective function subject to a series of linear constraints about the column profile of the parity check matrix. One major drawback of this method is that an important parameter, the average column (row) weight, does not participate in the optimization since the average column weight is determined as soon as a row profile is given We relax Davey's constraints of fixed row profile, using instead all the constraints about the row profile, the column profile and the average column weight and introduce the complex method to solve this nonlinear programming problem. This ensures that the average column weight of the parity-check matrix participates in the optimization.
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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 | 0.000 |
| 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 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".