Cross-Layer Adaptive Packet Scheduling over Fading Channel
Bibliographic record
Abstract
Cross-layer adaptive resource allocation techniques are found to be powerful techniques for achieving high throughput and high reliability over wireless fading channels. Recently, it has been revealed in the literature that cross-layer adaptation and optimization techniques can improve the overall system level Quality of Service (QoS) performance significantly over separate single layer adaptation and optimization techniques. In this chapter, the authors discuss the novel cross-layer techniques that jointly consider the physical layer channel gain as well as the upper layer buffer occupancy and traffic information in order to find transmission rate and power policies that jointly optimize transmission power, buffer delay, and packet overflow for an application specific bit error rate. They provide a conceptual study on the cross-layer adaptation and optimization techniques, which fuels necessary motivation and direction on how to implement them in different wireless standards and devices. The authors discuss the associated system modeling, problem formulation, and solution techniques as well as show the benefits of cross-layer adaptation and optimization techniques as compared to single-layer counterpart with numerical results.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".