Metric based node synchronization of the Viterbi decoder
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Bibliographic record
Abstract
Convolutional codes are used in digital communication systems to correct errors that occur during transmission. Before the decoder can correctly decode the received continuous bit sequence however, it must establish where codewords begin and end in this sequence. The process of determining codeword boundaries is called node synchronization. In this paper we develop a node synchronization algorithm based on metric difference values with the objectives of simple implementation and reliable performance. We consider the constraint length 7, rate 1/2 convolutional code standardized by the European Space Agency. We develop analytical results for the up/down counter framing algorithm, and through simulation investigate correlation in the sequence of metric values. Recommendations for framing parameters are based on both the analytical results and the response of the framing algorithm to correlated metric values. Simulations confirm that this simple approach results in reliable node synchronization.
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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 | 0.001 |
| 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