Joint handoff and resource management for throughput fairness in a wireless mesh network
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".