Implementation of an IEEE 802.11 link available bandwidth algorithm to allow crosslayering
Bibliographic record
Abstract
With the popularity and wide adoption of IEEE 802.11 equipment, wireless networks are being used in an increasing number of applications. Wireless links brings new challenges to communications protocols since link quality is unpredictable. To cope with this problem, many recent research proposals have employed cross-layering design. Often, the MAC layer is assumed to provide link quality metrics. Existing IEEE 802.11 radios and drivers do not provide detailed link quality metrics, which restrained a lot of cross-layering work to simulation environment. In this paper, we propose an algorithm that measures and computes link quality metrics inside IEEE 802.11 MAC so that it provides detailed link quality information to other layers of the protocol stack. Among other things, we implemented an algorithm that provides the available bandwidth to each neighbor node in an ad hoc network. This could be used in a number of scenarios to achieve work in the area of cross-layer design in real test beds. Typically, such work has been constrained to simulation or emulation environments due to the lack of link quality metrics provided by IEEE 802.11 MAC drivers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".