Multi-Attribute Network Selection by Iterative TOPSIS for Heterogeneous Wireless Access
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contemporary multimedia consumer devices are increasingly obtaining network connectivity mostly through wireless means. In order to economically support the mobile lifestyle of users, a new class of multimodal consumer devices has emerged that are equipped with heterogeneous wireless access capability. Inter-working of heterogeneous packet switched wireless networks, e.g., cellular and WLANs, via IP is a key step to provide ubiquitous service delivery via seamless connectivity of consumer devices. These wireless networks have a diverse range of capabilities and therefore selection of a specific network to optimize service delivery is an issue. Various algorithms have been proposed for use in the decision making process, with the class of Multi Attribute Decision Making (MADM) methods being one of the most promising. MADM methods, however, are known to suffer from ranking abnormalities. This paper applies TOPSIS, a MADM algorithm, to the problem of network selection. The causes of ranking abnormalities in TOPSIS are analyzed. An improvement to the algorithm as applied to the problem of network selection, where only the top ranking alternatives are considered important for decision making, is proposed. The new approach iteratively applies TOPSIS to the problem, removing the bottom ranked candidate network after each iteration. Simulation results are presented to demonstrate the effectiveness of the proposed iterative TOPSIS approach.
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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) | 0.000 | 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