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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".