Performance of MIMO cross‐layer MAC protocol based on antenna selection in <i>ad hoc</i> networks
Bibliographic record
Abstract
Abstract In this paper, we investigate the performance of a cross‐layer (physical and MAC) design for multiple‐input multiple‐output (MIMO) system that aims at maximizing the throughput of ad hoc networks by selecting the optimum antenna combination. Employing this cross‐layer design is shown to improve the overall network performance relative to the case where no antenna selection (AS) is used. To solve the node blocking problem associated with the IEEE 802.11 medium‐access control (MAC) protocol, the proposed protocol leverage the available degrees of freedom offered by the MIMO system to allow neighboring nodes to simultaneously communicate using the zero‐forcing (ZF) Bell‐labs layered space‐time (BLAST) architecture. Using the cross‐layer design, neighboring nodes share their optimum antenna selection (AS) information through control messages. Given this shared information, nodes set their decisions on the number of selected antennas based on the available spatial channels that guarantees collision‐free transmissions. At the destination node, the ZF receiver is employed to extract the desired user data while treating the data from neighboring users as interference. The performance of the proposed cross‐layer design is examined through simulations, where we show that the network throughput is significantly improved compared to conventional MAC protocols. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".