Performance Analysis of Downlink MU-TXOP Sharing in IEEE 802.11ac
Bibliographic record
Abstract
The 802.11ac version of the popular IEEE 802.11 protocol aims to boost performance by increasing the channel bandwidth and allowing downlink multiuser multiple-input multiple-output (MIMO) - i.e., simultaneous transmission to multiple clients in the downlink direction. In this paper, we evaluate the performance improvements of multiuser Transmission Opportunity (MU-TXOP) sharing in this scenario under nonsaturated load and non-ideal channel condition, using a novel analytical model based on discrete Markov chain and E-limited M/G/1 queuing model. Our results show that MU-TXOP sharing achieves up to 105% gain in throughput compared to the case without MU-TXOP sharing. Moreover, the low-priority traffic benefits most from MU-TXOP sharing, with up to 150% throughput gain at low traffic intensity due to availability of additional transmission opportunities for secondary ACs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".