Exact analysis on network capacity of airborne MANETS with digital beamforming antennas
Bibliographic record
Abstract
Recently, the use of digital beamforming (DBF) antennas has been drawing lots of interest in airborne platforms for resolving the network partition problem in airborne mobile ad hoc networks (MANETs). In this paper, properties of the network capacity of an airborne MANET with DBF antennas are investigated. This paper considers an ad hoc network consisting of a number of uniformly distributed airborne platforms in a bounded area. These platforms are either directly or indirectly connected with each other through DBF antennas, and form an airborne MANET. We first formulate a digital beamforming antenna system model, referred to as the omni-direction plus sector (OPS) model, and then carry out an exact analysis for the network capacity. The OPS model characterizes the radiation pattern of a DBF antenna. Under the condition that a Hamiltonian path exists in the network, an explicit expression is derived for the network capacity. We show that, for fixed values of the OPS model, the network capacity increases as the network size increases until it reaches an optimal value. If the network size continues to increase, the network capacity will decrease until it reaches zero. Explicit expressions for the optimal network size and the maximum network capacity are also obtained. Finally, numerical results are presented. It is shown that both the optimal network size and the maximum network capacity increase as the antenna beamwidth decreases. They increase slowly when the beamwidth is large, but as the antenna beam becomes narrower, they increase faster.
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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 | 0.001 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".