Capacity deficit and link loss in wlan to cellular vertical handoff
Bibliographic record
Abstract
Mobile handset manufacturers have begun to include wireless LAN (WLAN) interfaces in their cellular handsets. This allows users to access WLAN networks when they are available and to revert to conventional cellular communications otherwise. In this way the handset can dynamically use the "best" available network, by switching connections between the two network interfaces. This switching is referred to as a vertical handoff (VHO). When handling real-time connections, handsets must be capable of performing a seamless vertical handoff. This occurs when the interface switching does not disrupt the quality of service requirements of the active connections. Vertical handoffs are generally time consuming, and this delay creates a difficult problem since WLAN coverage can be lost very abruptly. In this thesis, we propose and investigate several methods of mitigating this problem. A solution based on using a Vertical Handoff Support Node (VHSN) is proposed. When the WLAN link is lost, the VHSN is able to quickly redirect packets through the local cellular base-station during the time that handoff is taking place. This approach can eliminate VHO link loss. It is shown that the act of WLAN-to-cellular handoff can result in a severe bandwidth deficit problem on the WLAN. A novel bandwidth reservation and securing mechanism is proposed which overcomes this problem and performs significantly better than schemes based on modified versions of the static guard channel scheme used in cellular networks. The work is characterized by simulation and analytic models which investigate the key performance aspects of this type of system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".