AP-STA Association Control for Throughput Maximization in Virtualized WiFi Networks
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Bibliographic record
Abstract
To manage and enable service customization among multiple internet service providers (ISPs) sharing the common physical infrastructure and network capacity in virtualized Wi-Fi networks, this paper models and optimizes access point-station (STA) association via airtime usage control. More specifically, an optimization problem is formulated on the STAs' transmission probabilities to maximize the overall network throughput, while providing airtime usage guarantees for the ISPs. As the proposed optimization problem is inherently non-convex, an algorithm to reach the optimal solution is developed by applying monomial approximation and geometric programming iteratively. Based on the proposed 3-D Markov-chain model of the enhanced distributed channel access protocol, the detailed implementation of the optimal transmission probability of each STA is also discussed by manipulating medium access control parameters. The performance of the developed association and airtime control scheme is evaluated through numerical results. For both homogeneous and non-homogeneous STA distributions, numerical results reveal performance gains of the proposed algorithm in improving the throughput and keeping airtime usage guarantees.
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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 | 0.001 | 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 | 0.001 | 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