A serial design of iterative belief propagation decoders for convolutional codes
Bibliographic record
Abstract
The belief propagation (BP) decoding algorithm may be suitable for the decoding of convolutional self-orthogonal codes which were originally proposed for one-step threshold de- coding. In this paper, a serial design of iterative BP decoder for convolutional self-orthogonal codes is presented. Using the alge- braic structures of convolutional codes, the iterative BP decoder is designed as a serial concatenation of several one-step BP de- coders. These one-step BP decoders are implemented using mainly the shift registers in a structure similar to that of type-II threshold decoders. The iterative BP decoder performs a non- trellis-based forward-only algorithm and has only an initial de- coding delay, thus avoiding intermediate decoding delays that usually accompany BP or turbo decoding of data frames. As shown by simulation results, the use of weighing techniques has provided substantial improvements to the error performance of the iterative BP decoding at a cost of several multipliers in hard- ware implementation. The iterative BP decoder may be attrac- tive to the practical applications in very high data rate areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".