Reconfigurable Transmitarray Design Approaches for Beamforming Applications
Bibliographic record
Abstract
Recent advances in wireless sensing and communications have resulted in the need for antennas capable of high-directivity reconfigurable beamforming. Transmitarrays have been shown to be viable architectures, and two general design approaches have emerged: the layered-scatterer approach and the guided-wave approach. In this paper, we first investigate the beamforming capability of the layered-scatterer approach, generalizing the approach using impedance surfaces. Using Floquet mode analysis, we show that when a structure of this type is used to produce a pencil beam at angles greater than 20 degrees off-broadside, significant side-lobes are produced at large angles, regardless of the aperture size. Next, we present a fully reconfigurable 6 × 6 transmitarray based on a guided-wave approach that experimentally demonstrates both pencil beam scanning over a 100 × 100-degree window as well as shaped-beam synthesis, over a 10 percent fractional bandwidth. The conclusion from our investigations into these two approaches is that for general beamforming applications, the guided-wave approach is superior, achieving good bandwidth, scanning range, insertion loss, and small structure thickness, with only moderate fabrication complexity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 0.002 |
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".