IEEE 802.11 medium access control enhancements based on simultaneous multiple‐input multiple‐output bandwidth sharing
Bibliographic record
Abstract
ABSTRACT The demand for higher data rate has spurred the adoption of multiple‐input multiple‐output (MIMO) transmission techniques in IEEE 802.11 products. MIMO techniques provide an additional spatial dimension that can significantly increase the channel capacity. A number of multiuser MIMO system have been proposed, where the multiple antenna at the physical layer are employed for multiuser access, allowing multiple users to share the same bandwidth. As these MIMO physical layer technologies further evolve, the usable bandwidth per application increases; hence, the average service time per application decreases. However, in the IEEE 802.11 distributed coordination function‐based systems, a considerable amount of bandwidth is wasted during the medium access and coordination process. Therefore, as the usable bandwidth is enhanced using MIMO technology, the bandwidth wastage of medium access and coordination becomes a significant performance bottleneck. Hence, there is a fundamental need for bandwidth sharing schemes at the medium access control (MAC) layer where multiple connections can concurrently use the increased bandwidth provided by the physical layer MIMO technologies. In this paper, we propose the MIMO‐aware rate splitting (MRS) MAC protocol and examine its behavior under different scenarios. MRS is a distributed MAC protocol where nodes locally cooperate with one another to share bandwidth via splitting the spatial channels of MIMO systems. Simulation results of MRS protocol are obtained and compared with those of IEEE 802.11n protocol. We show that our proposed MRS scheme can significantly outperform the IEEE 802.11n in medium access delay and throughput. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".