A 3D-Printed DRA Shared-Aperture Array for Low Cost Millimeter-Wave Applications
Bibliographic record
Abstract
In this article, a K-/Ka-band shared-aperture array with high isolation is proposed using 3D-printed dielectric resonator antennas (DRAs). The design procedure with DRAs enables flexibility while implementing shared-aperture arrays. Furthermore, DRAs as nonmetallic radiators are attractive for millimeter-wave (mm-wave) applications due to the absence of ohmic loss. In addition, 3D-printing technology facilitates the design of complex shapes with high-dimensional accuracy. The K- and Ka-bands DRAs are orthogonally oriented to provide improved isolation and cross-polarization level. Meanwhile, the corresponding feeding networks are implemented using the substrate integrated coaxial line (SICL) and the substrate integrated waveguide (SIW). Thus, further isolation improvement is provided by using two different feeding mechanisms for the K- and Ka-bands radiators. Moreover, the SICL network is shared with the SIW wall to efficiently utilize the feeding aperture. As a proof-of-concept, the 3D-printed shared DRAs are designed and fabricated. The standard printed circuit board (PCB) technology is used for the fabrication of the antenna boards. The measurement data agree with the simulations, realizing high isolation and cross-polarization level. The proposed array features low profile, ease of integration, and wideband operation, which makes it viable candidate for mm-wave applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".