Prediction-based admission control for DiffServ wireless Internet
Why this work is in the frame
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Bibliographic record
Abstract
Wireless Internet consists of different wireless technologies that should operate together in a consistent way to provide seamless quality of service to wireless users. In this paper, a wireless cellular architecture overlaid with DiffServ domains is considered. We propose a flexible hierarchical framework for admission control based on this architecture which aims to keep the handoff dropping probability below a target level while maximizing the network utilization. The novelty of our proposal is that (1) our prediction-based admission control scheme considers not only intra-domain but also inter-domain handoffs, while (2) it is based on on-line bandwidth requirement prediction, and (3) benefits from different priorities among different service classes to improve the network utilization by accommodating high-priority handoffs at the expense of dropping low-priority calls. Simulation results show that our scheme outperforms the basic trunk reservation scheme with domain and cell-level reservations.
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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 | 0.001 | 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 | 0.000 |
| 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