Optimal Voice Admission Control Performance under Soft Vertical Handoff in Loosely Coupled 3G/WLAN Networks
Bibliographic record
Abstract
Low cost WLAN networks are very likely to be loosely coupled to high cost 3G networks in order to provide the 3G subscribers with an extended WLAN capacity and the WLAN subscribers with the 3G ubiquitous coverage. Considering the high signaling latency when handing off critical voice calls between the two loosely coupled networks, soft vertical handoff (VHO) has already been introduced to ensure packet-level QoS however its resource efficiency is not evaluated yet. Therefore, in this paper we study the impact of mobility and soft VHO on the performance of the optimal voice admission control in terms of blocking probabilities. First, we propose an accurate analytical model for our system. Then we show that a resource-efficient soft handoff (RESHO) algorithm provides much better performance than a static-threshold soft handoff (STSHO) algorithm in WLAN mobility environments. In fact, we observe that the 3G new call blocking probability reduction gained by using a RESHO algorithm compared to a STSHO algorithm is largely increased when multi-mode mobile station velocities have low mean and high variability. This velocity profile typically characterizes the WLAN mobility environment. We believe that the provided model and the presented results could push and help the design of highly efficient soft VHO algorithms for 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".