A Study on Vertical Handoff for Integrated WLAN and WWAN with Micro-Mobility Prediction
Bibliographic record
Abstract
The integration of the third generation (3G) wireless wide-area networks (WWAN) and the IEEE 802.11 wireless local-area networks (WLAN) has drawn much attention from both industry and academia. To achieve an effective and efficient integration between the two networks with very different characteristics, nonetheless, is still an open issue. One of the challenges is to provide an integrated strategy for achieving a seamless vertical handoff of mobile users roaming between the two network domains where the delay, delay jitter, and packet loss probability can be well controlled. This paper is committed to study a two-step vertical handoff mechanism based on linear regression which is further modeled through an analytical approach. The proposed vertical handoff scheme is characterized by its adaptability to different quality of service (QoS) requirements by manipulating a threshold on the expected handoff instant. A new approach of mobility analysis is introduced to facilitate modeling of vertical handoff delay by taking advantage of Markov chain techniques. We have seen merits gained in our scheme in achieving a good trade-off between the average handoff delay and the multi-tunnel time by manipulating a threshold value, where both analytical and simulation results prove the effectiveness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".