Simplified LLR-based Viterbi decoder for convolutional codes in symmetric alpha-stable noise
Bibliographic record
Abstract
The design of a simplified Viterbi decoder for signals in symmetric alpha-stable noise is considered. The conventional Viterbi decoder, which has a branch metric optimized for Gaussian noise, performs poorly in symmetric alpha-stable noise. Since the optimal maximum likelihood (ML) branch metric is impractically complex, simplified approaches are needed. A simple 1-norm nonlinearity has been used instead of the Euclidean distance in the Gaussian branch metric to improve the performance of the Viterbi decoder. It shows performance improvement for higher values of alpha; however, the performance degrades when alpha approaches 1. In this paper, we propose a simplified branch metric which depends on a piecewise linear approximation of the log likelihood ratio (LLR). The Viterbi decoder with the proposed branch metric gives near-optimal performance for different values of alpha at low complexity. The simulation results show that the performance improvement of the Viterbi decoder with the proposed branch metric is approximately 1.5-4 dB compared to the Viterbi decoder with the 1-norm nonlinearity for different values of alpha.
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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".