Low-complexity near-optimal map decoder for convolutional codes in symmetric alpha-stable noise
Bibliographic record
Abstract
The design of the MAP decoder for signals in impulsive noise modeled using the symmetric α-stable (SαS) distribution is considered. The conventional MAP decoder, which optimizes the a posteriori probability for Gaussian noise, performs poorly in SαS noise. On the other hand, the optimal MAP decoder possesses impractical complexity due to the lack of a closed form expression of the probability density function. To simplify the implementation of the MAP decoder, the Huber nonlinearity was previously proposed, which results in a performance improvement over the conventional Gaussian MAP decoder. However, the performance is still far from optimal. In this paper, a simple unified approach to design low complexity suboptimal MAP decoder is proposed. The proposed approach uses the log likelihood ratio (LLR) as a metric to evaluate how close the suboptimal MAP decoder from the optimal. Based on this approach, a piecewise linear approximation of the LLR is used to design a sub-optimal MAP decoder which gives near optimal performance with low implementation complexity. The performance improvement of the MAP decoder is approximately 2-6 dB for different values of α compared to the MAP decoder with the Huber nonlinearity, with low complexity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".