Theoretic perspective of a wireless media access control scheme for small contention window sizes
Bibliographic record
Abstract
Under a distributed media access control (MAC) scheme, wireless stations select random time slots for transmission upon observing an idle channel. Performance of a distributed MAC scheme often depends on its ability to minimize collisions by reducing simultaneous data packet transmissions by two or more stations in any particular time slot. For this sake, each wireless station withholds its transmission until it observes a random number of idle slots when channel becomes idle. A station selects time slot for transmission based on a random number generated from a range of values in contention window (CW). As all stations independently generate their random value from their CW range, a large CW size is desirable for better channel arbitration. This is because, small CW sizes will lead to more number of collisions, and higher collisions will reduce throughput performance. This paper discusses and analyzes a scheme that can enhance throughput performance by reducing collisions under small CW sizes. The presented scheme is applicable to "Enhanced distributed channel access (EDCA)" protocol in which highest priority AC_VO queue uses very small CW sizes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".