Comparison between Vertical Handoff Decision Algorithms for Heterogeneous Wireless Networks
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Bibliographic record
Abstract
The next generation wireless networks will support the vertical handoff mechanism in which users can maintain the connections when they switch from one network to another (e.g., from IEEE 802.11b to CDMA 1timesRTT network, and vice versa). Although various vertical handoff decision algorithms have been proposed in the literature recently, there is a lack of performance comparisons between different schemes. In this paper, we compare the performance between four vertical handoff decision algorithms, namely, MEW (multiplicative exponent weighting), SAW (simple additive weighting), TOPSIS (technique for order preference by similarity to ideal solution), and GRA (grey relational analysis). All four algorithms allow different attributes (e.g., bandwidth, delay, packet loss rate, cost) to be included for vertical handoff decision. Results show that MEW, SAW, and TOPSIS provide similar performance to all four traffic classes. GRA provides a slightly higher bandwidth and lower delay for interactive and background traffic classes
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it