Efficient Methods for Performance Evaluations of Call Admission Control Schemes in Multi-Service Cellular Networks
Bibliographic record
Abstract
Many dynamic call admission control (CAC) schemes have been proposed in the literature for adaptive reservations in cellular networks. Efficient application of these schemes requires reliable and up-to-date feedback of system performance to the CAC mechanism. However, exact analyses of these schemes in real time using multi-dimensional Markov chain models are challenging due to the need to solve large sets of flow equations. One dimensional Markov chain models have been widely used to derive performance metrics such as call blocking probabilities of multiple traffic classes assuming that all classes of calls have equal capacity requirements and exponentially distributed channel holding times with equal mean values. These assumptions need to be relaxed for a more general evaluation of CAC performance in multi-service cellular networks. In this paper we classify CAC schemes according to their Markov chain models into two categories: symmetric and asymmetric, and develop computationally efficient analytical methods to compute call blocking probabilities of various traffic classes for several widely known CAC schemes under relaxed assumptions. We obtain a product form solution to evaluate symmetric schemes and propose a novel performance evaluation approximation method with low computational cost for asymmetric schemes. Numerical results demonstrate the accuracy and efficiency of the proposed method.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".