LDPC Decoding Over Nonbinary Queue-Based Burst Noise Channels
Bibliographic record
Abstract
Iterative decoding based on the sum-product algorithm (SPA) is examined for sending low-density parity check (LDPC) codes over a discrete nonbinary queue-based Markovian burst noise channel. This channel model is adopted due to its analytical tractability and its recently demonstrated capability in accurately representing correlated flat Rayleigh fading channels under antipodal signaling and either hard or soft output quantization. SPA equations are derived in closed form for this model in terms of its parameters. It is then numerically observed that potentially large coding gains can be realized with respect to the Shannon limit by exploiting channel memory as opposed to ignoring it via interleaving. Finally, the LDPC decoding performance under both matched and mismatched decoding regimes is evaluated. It is shown that the Markovian model provides noticeable gains over channel interleaving and that it can effectively capture the underlying fading channel behavior when decoding LDPC codes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".