Neural Successive Cancellation Decoding of Polar Codes
Why this work is in the frame
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Bibliographic record
Abstract
Neural network (NN) based decoders have appeared as potential candidates to replace successive cancellation (SC) based and belief propagation (BP) decoders for polar codes, due to their one-shot-decoding property. Partitioned NN (PNN) decoder has provided a solution to make use of multiple NN decoders which are connected with BP decoding, with the presence of insufficient training data for practical-length polar codes. However, PNN decoder requires BP iterations that detrimentally affect the decoding latency as compared to noniterative approaches. In this paper, we propose a neural SC (NSC) decoder to overcome the issue associated with PNN. Unlike PNN, the NSC decoder is constructed by multiple NN decoders connected with SC decoding. Compared to a PNN decoder for a polar code of length 128 and rate 0.5, the proposed NSC decoder achieves the same decoding performance, while reducing the decoding latency by 42.5%.
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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 it