Cross-layer analysis of wireless LANS: Backoff strategies and error control
Bibliographic record
Abstract
The medium access control frame duration is limited in wireless local area networks. Severalmodels have been applied to get better utilization of this frame. The frame is composed of different phases and adjusting the use of one phasemay effect the other phases' durations. WiMAX and IEEE802.11 standards have similar physical layer. Channel error due to noise or fading is another problem encounters the safe data delivery to the receiver. Therefore, several schemes have been implemented to deliver safe data to the receiver. In this paper, we propose some analytical models to reduce the contention on the random access phase by developing different backoff strategies. We also develope an error control model for safe data delivery. This is a cross-layer model where we apply our backoff strategies and error control model. The performance matrices for these models are measured by the throughput, acceptance probability, access delay, energy, average number of retransmissions and efficiency.
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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 | 0.001 |
| 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".