Procedures for Efficient Iterative Decoding of Orthogonal Convolutional Codes
Bibliographic record
Abstract
A procedure for the forward-only iterative belief propagation decoding of orthogonal convolutional codes is presented. It can be dramatically simplified to perform the iterative threshold decoding of convolutional self-doubly-orthogonal codes without interleaving. These procedures can help implement iterative decoders efficiently using a serial concatenation of one-step BP decoders or one-step threshold decoders, respectively. Simulations have shown that the error performance of orthogonal convolutional codes can be improved by iterative decoding whether based on belief propagation decoding or threshold decoding. For convolutional self-doubly-orthogonal codes, iterative threshold decoding can achieve the same error performance as iterative belief propagation decoding, but with greatly reduced decoding complexity, allowing an advantageous tradeoff between implementation complexity and latency.
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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 | 0.000 | 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 | 0.002 | 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".