A Fast Collision Detection Algorithm in IEEE 802.11 through Physical Layer SINR Monitoring
Bibliographic record
Abstract
Adaptive transmission in IEEE 802.11 requires rapid rate adaptation according to the variation of wireless environments. Existing rate adaptation schemes such as Automatic Rate Fallback (ARF) have been commercially implemented due to its simplicity. However, collision detection, which is an essential requirement to avoid network congestion and maintain the quality of service (QoS), is not well investigated in these traditional IEEE 802.11 link adaptation schemes. In this paper, we propose a fast collision detection scheme based on tracking the changes of the signal to interference and noise ratio (SINR). With the help of a nonparametric order-based cumulative sum (CUSUM) algorithm, collisions can be detected within a delay of a few OFDM symbols. Simulation results show the effectiveness of our proposed scheme and demonstrate its flexible implementation in existing IEEE 802.11 systems to assist current rate adaptation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".