ESPRIT-Based Directional MAC Protocol for Mobile Ad Hoc Networks
Bibliographic record
Abstract
The use of directional antennas in mobile ad hoc networks has shown to offer large potential 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) which, typically, requires a line of sight in order to avoid the large signal attenuation and hence is not suitable for indoor applications. Moreover, as the inaccuracy associated with the GPS position estimation increases, the system throughput dramatically degrades. In this paper, we propose an efficient two-channel two-mode DMAC protocol. Our protocol employs two frequency division multiplexed channels: channel one used for omni-mode transmission and channel two for directional mode transmission. Signal parameter estimation via the rotational invariance technique (ESPRIT) is used for direction-of-arrival (DOA) estimation. By avoiding the reliance on GPS for obtaining the position information, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results clearly show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".