WLC17-1: Performance Analysis of a Reservation Based Connection Admission Scheme in 802.16 Networks
Bibliographic record
Abstract
There is an increasingly growing interest in the IEEE 802.16 owing to its unique and useful characteristics in offering broadband wireless access. One of the key strengths is its support for different quality of service (QoS) classes which positions it to support high-quality voice, video, and data services. Connection admission control (CAC) plays an important role in fulfilling service differentiation and QoS satisfaction defined in IEEE 802.16 Std.. A traditional CAC scheme, known as complete sharing, is expected unable to explore the maximum advantage in using the IEEE 802.16 networks since it does not take the priority of different service classes into account. In this paper, a reservation based CAC scheme is introduced. By considering the service differentiation defined in the IEEE 802.16 networks, the proposed scheme can provide significantly lower connection block probabilities for higher priority services, which leads to better revenue. We analyze the proposed scheme in terms of some importance performance metrics, such as connection block probability for different service classes, bandwidth utilization, and the revenue generated at the BS. The analysis and simulation results are given to illustrate the efficiency of the proposed scheme and the accuracy of the analysis.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".