Techniques for early stopping and error detection in turbo decoding
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, we present three new criteria for early stopping and error detection in turbo decoding. The approaches are based on monitoring the mean of the absolute values of the log-likelihood ratio of the decoded bits, which we show to be directly related to the variance of the metachannel. We demonstrate that this mean value increases as the number of errors in a frame decreases, and as a result, propose the simple mean-estimate criterion. We show that the systematic component of a terminated recursive systematic convolutional encoder used in turbo codes provides a built-in cyclic redundancy check (CRC). To further improve the performance, we also propose the mean-sign-change (MSC) criterion and the MSC-CRCeb criterion, in which a short external CRC code and the built-in CRC are concatenated with the MSC criterion.
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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 | 0.000 | 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 it