A Practical Air Time Control Strategy for Wi-Fi in Diverse Environment
Bibliographic record
Abstract
802.11 (Wi-Fi) networks are widely deployed, providing access to a huge number of users using the unlicensed spectrum. Wi-Fi users have different bandwidth capabilities based on location, interference and application requirements. Given the way Wi-Fi accesses the wireless spectrum, tests in our lab and reports in the literature showed that low bandwidth Wi-Fi users take a big portion of the air time, hindering the performance of high bandwidth users. Some vendors address this with proprietary solutions that requires special drivers and chipsets. In this paper, we design and implement a portable solution that runs on any Wi-Fi device and doesn't require modifications to the hardware or the standard protocols. We propose different air time allocation strategies exploiting the Hierarchical Token Bucket (HTB) bandwidth management capability found in any Linux distribution. Weights and air quotas are calculated for different users based on a traffic shaping strategy. Compared to normal Wi-Fi access, tests showed that the proposed solution enables a flexible control of air time allocation to contending users without the need for proprietary drivers. Moreover, the results demonstrated an improvement of the throughput ratio and stability for most of the users, hence, making better use of the unlicensed spectrum.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".