On the support of voice call continuity across UMTS and wireless LANs
Bibliographic record
Abstract
Abstract In this paper, we address the architecture and the procedures that can enable voice call handover from UMTS to WLAN and we also study how efficiently the WLAN can support the voice calls transferred from UMTS. Our study is based on a practical simulation model that lets us quantify the maximum number of voice calls that can be handed over from UMTS to WLAN, subject to maintaining the same level of UMTS QoS and respecting some WLAN policies. In addition, several other voice call performance metrics are derived. Our results indicate that an IEEE 802.11e access point can support a limited number of voice calls handed over from UMTS, which depends primarily on the applied WLAN bandwidth sharing policy (i.e., how the bandwidth is shared between WLAN voice and data users) and the QoS requirements. The performance of the WLAN scheduling algorithm is also of paramount importance and in our study we consider the so‐called ARROW scheduler. Although the simulation results are derived for a specific bandwidth sharing policy, they can readily be scaled and provide practical upper bounds for the number of UMTS voice calls that can be seamlessly admitted to a WLAN access point. Copyright © 2007 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".