A Cooperative Two-Step Vertical Handoff Scheme with Mobility Prediction
Bibliographic record
Abstract
As mobile communication evolves into 3G beyond, the interworking of multiple heterogeneous networks serves as the major effort for taking the best advantage of different technologies available in supporting various emerging services, such as VoIP, Video on Demand (VoD), and IP Television (IPTV), etc. Vertical handoff is one of the key mechanisms in achieving Always Best Connected (ABC) for the mobile users by leveraging the benefits of deploying different types of networks for provisioning seamless handoff/roaming services in presence of user mobility. This paper aims to introduce a novel cooperative two-step vertical handoff scheme for the integration of 3G Wireless Wide-Area Networks (WWAN) and the IEEE 802.11 Wireless Local-Area Networks (WLANs). The proposed scheme is based on the cooperation based access point (AP) and mobile station (MS), where the AP manipulates the sensed signal strength to determine whether a pre-handoff action should be initiated. To improve the accuracy of user mobility prediction, a Markov model that incorporates with a novel parameter training process is developed at the AP for acquiring the hotspot geographic arrangement, such as the location of aisles, walls, and entrances/exits, etc., which is considered as the major factor of determining the user mobility patterns in an indoor hotspot. We will justify feasibility and discuss the operation complexity of the proposed cooperative vertical handoff. Moreover, error propagation due to inaccurate signal strength measurement is studied through Maximum Likelihood estimation. Finally, we will clearly demonstrate the merits gained by using the proposed two-step vertical handoff mechanism through extensive simulation, where the derived analytical models are verified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".