Optimized bandwidth allocation with fairness and service differentiation in multimedia wireless networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this article we present an optimal Markov Decision‐based Call Admission Control (MD‐CAC) policy for the multimedia services that characterize the next generation of wireless cellular networks. A Markov decision process (MDP) is used to represent the CAC policy. The MD‐CAC is formulated as a linear programming problem with the objectives of maximizing the system utilization while ensuring class differentiation and providing quantitative fairness guarantees among different classes of users. Through simulation, we show that the MD‐CAC policy potentially achieves the optimal decisions. Hence our proposed MD‐CAC policy satisfies its design goals in terms of call‐class‐differentiation, fairness and system utilization. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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