A utility based access point selection method for IEEE 802.11 wireless networks with enhanced quality of experience
Why this work is in the frame
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Bibliographic record
Abstract
Over the past ten years, many solutions have been proposed to address the problem of access point selection in IEEE 802.11 Wi-Fi networks. The standard, which recommends that user devices select an access point based on received signal strength (RSS) has many shortcomings and leads to poor performance. Many of the solutions proposed lead to better performance under some circumstances and with a particular goal in mind. However, in general, each solution has shortcomings as well. In this paper, techniques in access point selection are surveyed dating back to 2002. These approaches are compared and classified, and the problems and limitations are identified. Lastly, a utility-based method which is proposed which is generalized and may take into account a wide range of interests and goals. The performance of the proposed utility-based approach is evaluated with some preliminary simulations in ns3.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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