Deep Learning Techniques for Decoding Polar Codes
Why this work is in the frame
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Bibliographic record
Abstract
This chapter provides the background and motivation for the use of deep learning (DL) in various forward error correction schemes used for wireless communication systems. Polar codes are a recent breakthrough in the field of channel coding, as they were proven to achieve channel capacity with efficient encoding and decoding algorithms. Successive cancellation and belief propagation decoding algorithms are first introduced to decode polar codes. The chapter provides some basic knowledge about polar codes and conventional polar decoders. It then discusses several DL-based decoding algorithms and their variants for polar codes, followed by a detailed evaluation concerning error-correction performance and decoding latency of state-of-the-art DL-aided decoders for a 5G polar code. The chapter also describes the use of DL in decoding polar codes with emphasis on off-the-shelf DL decoders and DL-aided decoders by addressing their working principles, algorithm details, and performance evaluations.
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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 | 0.001 | 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 it