WLC40-5: Efficient Approximations for Call Admission Control Performance Evaluations in Multi-Service Networks
Bibliographic record
Abstract
Several dynamic call admission control (CAC) schemes for cellular networks have been proposed in the literature to reserve resources adaptively to provide the desired quality of service (QoS) to not only high priority calls but also to low priority ones. Efficient adaptive reservations depend on reliable and up-to-date system status feedback provided to the CAC mechanism. However exact analysis of these schemes using multidimensional Markov chain models are intractable in real time due to the need to solve large sets of flow equations. Hence performance metrics such as call blocking probabilities of various QoS classes are generally evaluated using one dimensional Markov chain models assuming that channel occupancy times for all QoS classes have equal mean values and all arriving calls have equal capacity requirements. In this paper we re-evaluate the analytical methods to compute call blocking probabilities of various QoS classes for several widely known CAC schemes by relaxing these assumptions, and propose a novel approximation method for performance evaluation with low computational complexity. Numerical results show that proposed method provides results that match well with the exact solutions.
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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 | 0.016 |
| 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 | 0.002 | 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".