The bandwidth deficit problem in loosely coupled WLAN-to-cellular vertical handover
Bibliographic record
Abstract
Seamless WLAN-to-cellular handover is often difficult to achieve, since WLAN coverage can often be lost very quickly. Recent results have shown that successful handover may require that the WLAN link be allowed to rate-adjust itself to very low values (e.g., 1-2 Mbps) before the vertical handover is completed. When this occurs the call temporarily occupies a WLAN bandwidth far higher than the value for which it was originally provisioned. This effect is referred to as the vertical handover bandwidth deficit problem. In this paper we consider the effects of the bandwidth deficit problem on system performance, and how the problem can be mitigated. It is shown that this bandwidth demand can result in significant vertical handover dropping due to a lack of bandwidth on the WLAN access point. A static bandwidth reservation scheme motivated by the classical cellular guard channel approach is first considered and is found to result in an unacceptable increase in the WLAN new call blocking rate. A novel transient bandwidth reservation scheme accompanied by a momentary forced handover mechanism is proposed to overcome the shortcomings of the static approach in multiple AP WLANs. Our presented results show that almost two orders of magnitude reduction in the vertical handover dropping rate can be achieved while maintaining an acceptable new call blocking rate at the WLAN AP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".