On high-throughput and fair multi-hop wireless ad hoc networks with MIMO
Bibliographic record
Abstract
Nowadays, the prevalence of multimedia applications mandates that wireless networks provide higher throughput and increased spatial efficiency. Aimed primarily at achieving this goal, the first IEEE 802.11n draft was recently approved. In the draft, MIMO and frame aggregation are utilized to improve the PHY and MAC layers in single-hop WLANs by achieving high raw rates and goodput. However, in the context of multi-hop wireless ad hoc networks, the hidden terminal, exposed terminal, and deafness problems remain unsolved by IEEE 802.11n. Consequently, this greatly degrades the MAC efficiency in multi-hop wireless ad hoc networks, even in the presence of frame aggregation. In this paper, we propose a novel MAC scheme that utilizes MIMO distributed spatial multiplexing to solve all the aforementioned problems, resulting in high throughput and fairness in multi-hop wireless ad hoc networks. Moreover, our scheme can be integrated with frame aggregation to further enhance the network performance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".