The impact of time varying channels on MAC layer in IEEE 802.11 networks
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Bibliographic record
Abstract
Channel variation plays a crucial role in data transmission. This variation may affect wireless network protocols like IEEE 802.11 and hence the throughput. We propose the combination of a simple channel estimation method based on basis expansion model with channel prediction to improve the performance of the IEEE 802.11 network's MAC protocols in a time-varying channel environment. When many users try to access the medium, the time separation (delay) between two successive transmissions belonging to the same user may vary due to the fairness mechanism used by the medium access protocols. During this delay, the channel may change and efficient channel modeling and estimation methods should be applied. Time varying channel's estimation and prediction have been proposed in order to mitigate the effect of channel variation on the system's throughput. This proposition mitigates also the impact of pilot signals loss, due to collisions, on the system's throughput. Pilot signals are known to the receiver and they are used to predict the future values of the timevarying channel for the following data reception period.
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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 | 0.000 |
| Open science | 0.004 | 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