On the impact of soft vertical handoff on optimal voice admission control in PCF-based WLANs loosely coupled to 3G networks
Bibliographic record
Abstract
Soft vertical handoff (VHO) and admission control are usually considered as two independent mechanisms ensuring respectively packet-level QoS and call-level QoS for voice calls in loosely coupled 3G/WLAN networks. In this paper, we evaluate the impact of the soft VHO on the blocking performance of the optimal voice admission control in different mobility environments where the WLAN operates the Point Coordination Function (PCF). For this purpose, we propose an accurate analytical mobility model for the soft VHO region. Then, based on the proposed model, we derive and analyze the blocking and dropping probability expressions of the optimal voice admission control algorithm in the 3G network loosely coupled to the PCF-based WLAN. Results show us that a resource-efficient soft handoff (RESHO) performs significantly better than a static-threshold soft handoff (STSHO) particularly in WLAN mobility environments. In fact, the 3G new call blocking probability reduction gained by using RESHO compared to STSHO is largely increased when mobile station (MS) velocities have low mean and high variability which typically characterizes theWLAN mobility environment. Besides, results show us that RESHO reduces all blocking and dropping probabilities.We believe that the provided model and the presented results could help design efficient MS controlled soft VHO algorithms for emergent loosely coupled 3G/WLAN networks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".