Efficient call admission control scheme for 4G wireless networks
Bibliographic record
Abstract
Abstract Next generation wireless networks (NGWNs) will utilize several different radio access technologies, seamlessly integrated to form one access network. This network has the potential to provide many of the requirements that other previous systems did not achieve such as high data transfer rates, effectives user control, seamless mobility, and others which will potentially change the way users utilize mobile devices. NGWN will integrate a multitude of different heterogeneous networks including (a) cellular networks, passed through multiple generations—1G, 2G, 3G, and 3.5G; (b) wireless LANs, championed by the IEEE 802.11 wireless fidelity (WiFi) networks; and (c) broadband wireless access networks (IEEE 802.16, WiMAX). In this paper, a new adaptive quality of service (QoS) oriented CAC scheme is proposed to limit the occurrence of hard IEEE 802.11 WLAN‐UMTS handovers to mobile users using real time (RT) applications. This scheme is hybrid, based on the service class differentiation, the location in the heterogeneous infrastructure and a vertical handoff decision function as well. Simulation results show that our policy achieves significant performance gains. It maximizes the utilization of the resources available at the WLAN cells, and meets as much as possible the QoS requirement of higher priority users. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".