Virtual Partitioning for Connection Admission Control in Cellular/WLAN Interworking
Bibliographic record
Abstract
Wireless wide area networks (WWANs) and wireless local area networks (WLANs) have complementary characteristics which make them suitable to jointly offer an ubiquitous wireless solution. In cellular/WLAN interworking, the quality of service (QoS) requirements for different services (e.g., voice and real-time video) can be guaranteed by using connection admission control. In this paper, we propose the use of virtual partitioning (VP) [S. Borst and D. Mitra] resource sharing scheme to facilitate admission control in a multi-service integrated cellular/WLAN system. VP pre-allocates a nominal capacity for each service based on the expected traffic and the required blocking probabilities. We first determine the policy functions corresponding to VP for new and handoff connection requests. Then, three different nominal capacities for VP are compared with the cutoff priority policy. Numerical results show that lower blocking and dropping probabilities can be achieved by VP in a wide range of conditions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".