Distributed schemes for fair throughput in infrastructure-based IEEE 802.11 mesh networks
Bibliographic record
Abstract
This paper studies throughput fairness among different basic service sets (BSSs) in infrastructure-based IEEE 802.11 mesh networks, where inter-BSS interference is unavoidable because of the difficulty in frequency and coverage planning and the limited number of non-overlapping frequency channels available for IEEE 802.11 networks. Two approaches are proposed for achieving fair throughput among the co-channel BSSs: (i) dynamic network allocation vectors (DNAV), which dynamically adjusts the co-channel interference by temporarily stopping transmissions in some BSSs using network allocation vectors (NAVs), and (ii) forced handoffs (FHO), which forces mobile stations (MSs) to handoff between neighboring BSSs. An optimization problem is formulated as well as a distributed scheme is proposed for each of the approaches for achieving the required throughput fairness while keeping high transmission throughput in the BSSs. Our results show that (i) the DNAV scheme can always achieve the required throughput fairness; (ii) performance of throughput fairness using the FHO scheme depends on coverage overlapping between the BSSs, and when there is strong coverage overlapping among the BSSs, the scheme achieves a higher throughput than the DNAV while achieving the required throughput fairness; and (iii) the converged throughput performance of the proposed distributed schemes is very close to the corresponding optimized one.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".