Decoding schedule generating method for successive‐cancellation decoder of polar codes
Bibliographic record
Abstract
Successive cancellation (SC) is the first and widely known decoder of polar codes, which has received a lot of attentions. However, its decoding schedule generating methods are still primitive. Based on a newly found factor, this study proposes an online method to generate the decoding schedule of SC decoder by a non‐recursive way. The decoding schedule of SC decoder includes two parts. One is to determine which of likelihood ratios (LRs) can be shared and which of LRs need to be calculated. The other is to select a calculation formula for a LR to be computed. As shown by the comparisons among the proposed method and existing methods, the proposed method solves the first part of the decoding schedule with constant critical path delay and lower space complexity, and solves the second part of the decoding schedule with less calculations. Besides, experimental results show that the proposed method does not affect the error performance of polar codes.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| 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".