Cross-layer optimization of rateless coding over wireless fading channels
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".