Fast Client-Based Connection Recovery for Soft WLAN-to-Cellular Vertical Handoff
Bibliographic record
Abstract
Wireless local area network (WLAN)-to-cellular vertical handoff (VHO) involves time-consuming procedures that may significantly disrupt real-time communication. When a soft handoff is anchored by an enterprise private branch exchange (PBX)/gateway, handoff execution can take several seconds due to the time required to establish the cellular-to-PBX call leg. This situation is compounded by the fact that WLAN coverage may sometimes be lost far sooner than a VHO can be triggered and executed. In this paper, we propose and investigate the use of a VHO support node (VHSN) which is attached locally to the wired LAN hotspot infrastructure. When WLAN coverage is lost, the VHSN improves soft-handoff performance by quickly intercepting and redirecting the media flow through the local cellular base station using its cellular end-station attachment. This action can quickly recover the connection before the new cellular call leg is established. Unlike proxy forwarding, wireless multihop, and other infrastructure-modification schemes, the VHSN does not extend wireless coverage or perform infrastructure-based packet forwarding. Instead, the VHSN functions as both an (Ethernet) LAN and a cellular end station. Results will be presented which show the performance improvements that are possible using the proposed mechanism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".