Adaptive access control of CSMA/CA in wireless LANs for throughput improvement
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an adaptive access scheme for Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) aiming to take advantage of multi-user diversity and improve throughput, while supporting distributed and asynchronous operation. By assigning channel-adaptive access probabilities to different users, this method prioritizes users who gain most from using a channel, and hence, improves channel utilization in comparison with a simple random access scheme. Furthermore, in this method, access probabilities are designed to achieve long-term fairness by keeping a same average access probability for all users. Performance of the proposed adaptive CSMA/CA is evaluated in terms of collision probability and saturation throughput by analysis and simulation. Illustrative and analytical results show that A-CSMA/CA significantly improves the throughput by controlling contention among users and decreasing the collision probability, specifically in a large network.
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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 | 0.001 |
| 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 it