Bibliographic record
Abstract
Abstract In this paper, two decoding schemes for polar codes based on the belief propagation (BP) algorithm are proposed. The basic idea of the proposed schemes, called “interleaved BP” (I‐BP) and “multiple‐candidates BP” (M‐BP), is to construct multiple candidates with different reliability values from the received signal and to decode each candidate by a BP decoder. Then, the output of the BP decoder that meets the stopping criterion or the maximum likelihood (ML) rule is chosen as the decoded data. Simulation results show that both the proposed polar decoders outperform the one based only on a single conventional BP decoder. In conjunction with each of the proposed schemes, a feedback structure is also proposed to achieve more performance gain. The proposed feedback structure takes as input the output of each BP decoder, and enhances the a posteriori information of reliable bits and flips unreliable bits. Then, the processed information is fed back into its corresponding decoder. Simulation results show that the performance gain of the proposed schemes with this feedback, compared to the ones without the feedback, may be as large as 1 dB at a frame‐error rate on frequency selective channels and 2 dB at a frame‐error rate of 0.07 on doubly selective channels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".