Joint AMC and Packet Fragmentation for Error Control Over Fading Channels
Bibliographic record
Abstract
Error control is critical for wireless networks to combat channel fading and ensure efficient resource utilization. Adaptive modulation and coding (AMC) in the physical (PHY) layer and packet fragmentation and automatic repeat request (ARQ) in the link layer are widely used error-control mechanisms. However, how to jointly optimize them in both layers for high-rate wireless networks is still open. In this paper, using the WiMedia ultrawideband (UWB) networks as an example, we first develop a general analytical framework to quantify the link delay and loss performance considering the channel fading, the joint error-control mechanisms, and the arbitrary reservation-based media access control (MAC) protocol. Second, we introduce a cross-layer design to optimize the PHY-layer AMC and the link-layer packet fragmentation and propose a joint-adaptation mechanism that is simple to implement and has near-optimal performance. Numerical results reveal that fragmentation has a greater impact than AMC on the delay and loss performance for marginal links and that the proposed joint-adaptation strategy is efficient for high-rate wireless networks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".