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 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".