Bibliographic record
Abstract
Multiple input multiple output (MIMO) offers significant advantages in terms of rate and reliability. We consider sharing the MIMO increased rate in wireless mesh networks (WMNs) links. Such sharing allows nodes in a WMN to concurrently meet more demanding communication requirements such as high data rates, increased transmission range, power savings and low dropping rates. In this paper, we propose the rate splitting MIMO-based Mac protocol (RSMP) which is a distributed Mac protocol where pairs of nodes can locally cooperate with other nodes in their vicinities to reserve the required rate through effective selection on transmit antennas. Nodes can then use any preferable MIMO coding technique that best suit their communication requirements. RSMP is evaluated using OPNET interfaced with Matlab. OPNET is used to model the details of the Mac protocol while Matlab is used to compute the MIMO channel capacity and interference. Simulation results are obtained and compared to those of 802.11n MIMO-based DCF MAC protocol. We show that our proposed RSMP scheme outperforms MIMO DCF MAC in medium access delay and throughput. We also show that nodes can always attain their requested rate and that RSMP can be applied to satisfy multiple QoS requirements.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".