Advanced adaptive call admission control for mobile cellular networks
Bibliographic record
Abstract
The crucial issues for service providers in wireless cellular networks include providing guaranteed quality of service (QoS), minimizing dropping rate (DR) for handoff calls, reducing the blocking rate (BR) for new calls and most importantly ensuring efficient utilization of network resources to maximize profits. This paper proposes an adaptive call admission control (CAC) algorithm for a wideband code division multiple access (WCDMA) network. The proposed algorithm utilizes three major concepts: cell breathing by considering the distance of a new and handoff call from a base station, bandwidth degradation of multimedia calls to provide priority for voice and video calls and transfer of ongoing calls to other cells to provide the highest priority to voice calls. This work is useful in the case when a voice or video call cannot be accepted into a cell due to unavailability of bandwidth and also when a new or handoff voice call cannot be accepted because the cell has already reached its maximum number of calls. Simulation results are presented for relations between call blocking, dropping probability and bandwidth utilization for different types of traffic (i.e. multimedia, video and voice calls).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".