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 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 | 0.000 | 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".