Computationally Efficient Method to Evaluate the Performance of Guard-Channel-Based Call Admission Control in Cellular Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it