Complexity evaluation of sova based algorithms for decoding of block codes on a sectionalized trellis
Bibliographic record
Abstract
Turbo decoders are composed of two or more soft-input soft-output (SISO) decoders that exchange reliability information. A classical way of implementing a SISO decoder for a linear block code is to use a trellis-based decoding algorithm, such as MAP, Max-Log-MAP or SOVA [1]. Recently, the idea of sectionalizing the trellis diagram representing a code has been proposed as a means to decrease the decoding complexity associated with the SISO decoders [2-3]. In this paper, we investigate the application of a nonbinary SOVA decoding algorithm to the sectionalized trellis of a binary linear block code. Considering an (n, k) block code C, a minimal bit-level trellis has n sections. A ν-section sectionalized trellis with section boundaries U = {0, h 1 ,…, h v = n} is obtained from the bit-level trellis by removing any stage at a location not in U, and connecting the remaining states by branches whose labels are the labels of the paths originally connecting the nodes in the bit-level trellis (Our notations borrow from [1], so the reader is referred to this book for further details). Optimal sectionalization boundaries can be chosen in order to minimize the complexity in terms of operations and/or memory of a given algorithm for a particular code (such optimal sectionalizations have been derived for MAP [3], Max-Log-MAP [3] and Viterbi [2] decoding).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".