Joint handoff and resource management for throughput fairness in a wireless mesh network
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Bibliographic record
Abstract
In this paper we study the problem of providing fair throughput for mobile stations (MSs) in a wireless mesh network (WMN) by jointly considering the handoff management of the MSs and the resource allocations at the access points (APs). An optimization problem is formulated based on long-term proportional fairness, so that all the MSs in the entire WMN can receive fair average throughput, while the total throughput of the MSs is maximized. The optimum solution is based on the assumption of having global information about the channel conditions and mobility information of all the MSs, and cannot be easily implemented in a practical system. A heuristic scheme is then proposed, which allows each AP to allocate its resources based on local information only, and the handoff decisions of the MSs are based on information exchanged between neighboring APs. Numerical results show that performance of the proposed heuristic scheme is very close to the optimum in terms of both fairness and throughput.
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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