A Measurement-Based Study of WLAN to Cellular Handover
Bibliographic record
Abstract
Real-time vertical handover is an important capability for multimode WLAN/cellular handsets. In many cases however, seamless handover can be very difficult to achieve, since WLAN coverage may be lost long before a cellular call leg can be triggered and established. Worse-case handovers of this kind occur when mobile users walk from indoor building WLAN coverage to outdoors during voice connections. In this paper we report on a measurement-based study of WLAN-to-cellular handover. Our results are based on extensive IEEE 802.11 measurements that were made on the McMaster University campus during the summer of 2005. Our methodology involved traversing many indoor-to-outdoor paths for a large number of campus buildings and exits while monitoring multi-AP Wi-Fi coverage. The collected data was then processed to determine the probability of seamless handover using classical vertical handover algorithms. The results presented give important insights into the difficulty of this problem, and relate to issues such as Wi-Fi deployment type, handover triggering, and Wi-Fi link loss threshold. The results provided enable handset designers and WLAN administrators to better understand the sensitivity of vertical handover performance to these parameters and how they can be optimized
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".