A practical method for estimating performance metrics of call admission control schemes in wireless mobile networks
Bibliographic record
Abstract
Providing the desired call blocking probability (CBP) to not only new but also existing calls has been a challenge for wireless mobile network service providers. To satisfy different requirements for new and handoff CBP, several call admission control (CAC) schemes have been proposed in the literature. Exact analysis of these schemes using 2D Markov chains is computationally intensive. Therefore, computationally efficient methods to analyze these systems using 1D Markov chain models have been considered. The "traditional" approach assumes that channel holding time for new and handoff calls have equal mean values. While the "normalized" approach relaxes this assumption, it is accurate only for the new call bounding CAC scheme. In this paper, we reevaluate the analytical methods for CBP probabilities for several widely known CAC schemes under more general assumptions by providing an easy to implement method. The numerical results show that when the average values of channel holding times for new and handoff calls are different the proposed approach gives more accurate results when compared with the traditional and normalized methods based on 1D Markov chain modeling, while keeping the computational complexity low.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".