Estimating the minimum distance of large‐block turbo codes using iterative multiple‐impulse methods
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Bibliographic record
Abstract
Abstract A difficult problem for turbo codes is the efficient and accurate determination of the distance spectrum, or even just the minimum distance, for specific interleavers. This is especially true for large blocks, with many thousands of data bits, if the distance is high. This paper compares a number of recent distance estimation techniques and introduces a new approach, based on using specific event impulse patterns and iterative processing, that is specifically tailored to handle long interleavers with high spread. The new method is as reliable as two previous iterative multiple‐impulse methods, but with much lower complexity. A minimum distance of 60 has been estimated for a rate 1/3, 8‐state, turbo code with a dithered relative prime (DRP) interleaver of length K = 65 536. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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