Enhanced Bit-Flipping Successive Cancellation Decoding for Convolutional Polar Codes
Bibliographic record
Abstract
The paper presents an enhanced bit-flipping (BF) successive cancellation (SC) decoding method under limited cyclic redundancy checks (CRC). Although BF-SC decoding achieves comparable performance to SC list decoding via a series of SC decodings, the existing methods are inevitably impaired under limited CRC checks, which is the case during blind detection in physical downlink control channel. To this end, we propose a path-metric-assisted BF-SC method, which before CRC checks filters the clearly erroneous decodings and further prioritizes the ones more likely to be correct. Combined with convolutional polar codes - a recently-proposed encoding method with substantial coding gain but same $2x2$ kernel as the original polar, the proposed method is demonstrated to outperform the reference polar code settings in the current 5G standard even with limited CRC checks.
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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 | 0.001 | 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".