Performance of a MANET directional MAC protocol with angle‐of‐arrival estimation
Bibliographic record
Abstract
Abstract The use of directional antennas in mobile ad hoc networks (MANETs) has shown to offer large throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium‐access‐control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS). Although GPS systems are designed to be as nearly accurate as possible, there are still estimation errors that can cause a relatively large deviation from the actual GPS receiver position. In this paper, we investigate the effect of inaccurate node position estimation on the throughput of these protocols. Our results clearly indicate that the advantages of DMAC protocols diminish if the available position information is not accurate enough. As an alternative, we propose an efficient DMAC protocol that utilizes signal parameter estimation via the rotational invariance technique (ESPRIT) for direction‐of‐arrival (DOA) estimation; alleviating the need for GPS and, hence, avoiding the degrading associated with typical GPS position estimation errors. Moreover, unlike GPS‐based protocols, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11. Copyright © 2007 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 | 0.004 | 0.010 |
| 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".