A Fast Raptor Codes Decoding Strategy for Real-Time Communication Systems
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Bibliographic record
Abstract
We propose an efficient algorithm for Raptor decoding, which reduces the computational complexity of the most time-consuming steps in systematic decoding. Our proposed algorithm includes two aspects: First, to handle the decoding failure of the Raptor decoding, we propose a scheme, which is called the No-Wrapup Failure Handling scheme. It can resume the decoding process from where it fails after receiving a pre-defined number of additional encoded symbols, and thus avoids the repetition of time-consuming steps in the decoding process. Second, in order to reduce the time of finding the row with the minimum degree in the precode, we propose a Fast Min-Degree Seeking (FMDS) scheme. FMDS automatically maintains and updates the row degrees of the precode when converting the precode into an identity matrix through Gaussian elimination and Belief-propagation. Experimental results show that, compared to other Raptor decoding schemes, the proposed scheme achieves a much shorter decoding time, and can greatly speed up the data recovery in real-time applications.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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