Computationally Efficient Method to Evaluate the Performance of Guard-Channel-Based Call Admission Control in Cellular Networks
Bibliographic record
Abstract
Many guard-channel-based call admission control (CAC) schemes for cellular networks have been proposed to provide the desired quality of service to not only new calls but also ongoing calls when they hand off to neighboring cells. Blocking/dropping probabilities of new/handoff calls are generally analyzed using one-dimensional Markov chain modeling under specific assumptions to avoid solving large sets of flow equations that makes exact analyses of these schemes using multidimensional Markov chain models infeasible. This is the case with the "traditional" approach, which assumes that channel holding times for new and handoff calls have equal mean values, and the "normalized" approach, which relaxes this assumption but is accurate only for the new call bounding CAC scheme. In this paper, we reevaluate the analytical methods for computing new/handoff call blocking/dropping probabilities for several widely known CAC schemes and develop an easy-to-implement method under more general assumptions. Numerical results show that when the mean channel holding times for new and handoff calls are different, the proposed "effective holding time" approach gives more accurate results compared with the traditional and the normalized methods while keeping the computational complexity low. The accuracy of these methods and their levels of computational complexity with the exact solution are also compared
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".