Low Complexity Polar Decoder for 5G Embb Control Channel
Why this work is in the frame
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Bibliographic record
Abstract
Polar codes have become the channel coding scheme for control channel of enhanced mobile broadband in the fifth generation (5G) communication systems. Belief propagation (BP) decoding of polar codes has the advantage of low decoding latency and high parallelism but suffers from high complexity. In this paper, a low complexity BP decoder is proposed for polar codes. We reduce the computational complexity by two steps. First, the cyclic redundancy check is concatenated to the decoder in order to decrease the number iterations of the BP algorithm. Then, a threshold is proposed based on Gaussian approximation to save the computational complexity of BP nodes. If the log-likelihood ratio of a node in the tanner graph is larger than the threshold, this node is no longer updated during the rest of the decoding process. The simulation results show that the proposed scheme has a similar block error rate performance with the original BP decoder, while the computational complexity is reduced significantly.
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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 | 0.001 |
| Open science | 0.002 | 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