Cross-layer Raptor coding for broadcasting over wireless channels with memory
Why this work is in the frame
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Bibliographic record
Abstract
Raptor codes are a class of rateless codes that have been shown to provide promising performance in erasure channels, and more recently, in noisy channels. This paper investigates the performance of application layer Raptor codes for broadcasting services over wireless channels with memory. A hybrid erasure-soft decoding algorithm is proposed as a cross-layer protocol for application layer raptor codes. These protocols relay corrupted packets into the application layer. The resulting hybrid error-erasure channels are modeled by a hierarchical Markov channel model. Capacity evaluation and simulation results show that the proposed cross-layer decoding algorithms outperform existing erasure decoding schemes significantly without any modification to the transmitter. The effects of channel memory and other parameters are also studied by simulation.
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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 | 0.001 | 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