Cross-layer optimization of rateless coding over wireless fading channels
Why this work is in the frame
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Bibliographic record
Abstract
Rateless codes are recently-proposed erasure correction codes. To apply rateless codes over wireless communication channels, a physical-layer forward error correction (FEC) code, such as a convolutional code, is usually used to correct errors within each packet while Raptor codes are used in the application layer to correct erased packets. Traditionally, the physical-layer modulation and coding rate are chosen to guarantee an overall packet error rate to be below a certain level. However, such a choice does not always provide the best overall system performance. This paper proposes a cross-layer scheme to optimize physical layer modulation and coding rate to maximize system throughput. Both slow and fast fading channels are considered. For slow fading channels, cross-layer adaptive modulation and coding schemes are also proposed. Numerical results show that the proposed cross-layer schemes outperform traditional schemes significantly in terms of system throughput. The results also indicate that in many situations, allowing for more packet error correction in the application-layer through erasure codes can be more efficient than ensuring a low packet error rate using a low-rate physical-layer code.
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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