Optimal Configuration of Multi-Faceted Phased Arrays for Wide Angle Coverage
Bibliographic record
Abstract
As more users share the radio spectrum, communications systems become limited by interference. The situation can be improved by using smart antennas, including beam-scanning systems, for increased gain and interference suppression. The design of beam-scanning arrays typically involves a tradeoff between the coverage (the set of directions over which beams can be directed) and the scan angle. Wide coverage, which is typical for mobile communications, requires large scan angles from single arrays and this results in scan loss and greater cross-polarization which degrade the SNIR. Large coverage angles with low scan loss can only be realized with multi-faceted or conformal arrays. The multi-faceted arrays are simpler to manufacture. For hemispherical coverage, the basic array configurations are the pyramid and the pyramidal frustum. The design for the optimal geometry (face elevation angle) of pyramidal frustum arrays is addressed using a novel minimax-based approach and the methodology for the choice of the number of faces is presented. The coverage is the partial (rotationally symmetric) hemisphere. Applications for these wide coverage array systems are widespread, ranging from fixed and mobile satellite terminals, landmobile vehicular antennas, basestations for indoor and outdoor networks, to radar surveillance and radio astronomy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".