A Novel Call Admission Control in Multi-Service Wireless LANs
Bibliographic record
Abstract
A call admission control algorithm must try to admit as many calls as possible provided that their quality of service (QoS) requirements can be met without violating those of previously admitted calls. In this paper, we propose a simple and effective call admission control algorithm and its associated resource allocation mechanism ,referred to as the flexible call admission control, for recently proposed multi-pattern (MP) wireless local area networks (WLANs). The proposed flexible call admission control effectively takes the advantages of flexible pattern assignment in MP WLANs and the rate-adaptive feature of multimedia services to support multiple classes of traffic with diverse QoS requirements and priority levels. With the use of an innovative performance estimation mechanism, the proposed flexible call admission control and resource allocation algorithm has considerably lower complexity than that of the existing schemes. Simulation results have demonstrated that the use of MP FAC provides much higher system throughput and lower call blocking probability. It should be emphasized that the proposed FAC, although designed for MP WLANs, also works well with existing standard WLANs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".