Meta-Heuristic Solution for Dynamic Association Control in Virtualized Multi-Rate WLANs
Why this work is in the frame
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Bibliographic record
Abstract
Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.
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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 | 0.000 |
| 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