Designing a Sensible Block Semi-Random Interleaver for Turbo Codes
Why this work is in the frame
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Bibliographic record
Abstract
It is highly known that an interleaver (a device that scrambles the order of a sequence of numbers) is a key component of a turbo encoder to guarantee excellent bit error rate and frame error rate performances. Turbo codes were initially proposed using a randomly constructed interleaver. Turbo codes are a rank of high-performance forward error correction (FEC) codes, which were the initial practical codes to closely approach the channel capability. We introduce here a method for generating a sequence of semi-random interleavers, projected to be optimally stored and employed in a turbo coding system that requires litheness of the input block (i.e., interleaver) size. By the arrangement of construction and random search based on a careful analysis of the low weight words and the distance properties of the component codes, it is possible to find interleavers for turbo coding with a high minimum distance. We have designed a block semi-random interleaver with permutations of each row, and found a combination of permutations where a tight upper bound to the minimum distance of the complete turbo scheme is 108. By using our designed technique it is easier to include restrictions which make the interleaver correctly-terminating or odd-even. While the block semi-random interleavers serves well for specifying interleaver spread, we think our method will achieve better performance in a more sophisticated designed criteria.
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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 | 0.001 | 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 it