A 3D-Printed Encapsulated Dual Wide-Band Dielectric Resonator Antenna With Beam Switching Capability
Bibliographic record
Abstract
This paper presents the concept of encapsulated dielectric resonator antennas (E-DRAs). In E-DRAs, smaller-sized DRAs with a specific permittivity is embedded inside a larger DRA with a lower permittivity allowing for simultaneous efficient radiation at two widely separated and widely covered frequency bands. In this work, the proposed E-DRAs cover both the sub-6-GHz band (with a large size DRA) and mm-wave band (with smaller sized DRAs) for 5G and beyond applications. The proposed design of the dual wide-band E-DRAs is fabricated using the fused filament fabrication (FFF) 3D printing process. At mm-wave bands, small cylindrical DRAs (cDRAs) are the radiating elements, and a larger cDRA in conjunction with a dielectric lens (DL) is used to achieve high gain radiation at such high bands. An array of 5 elements is used in a switched mode fashion to add switching beam capability to the antenna at the mm-wave band. Employing 3D printing reduces the fabrication time and cost and enables precise control of the dielectric constant of the DRAs. Measurement results show a maximum gain of 7.2 dBi at 3.2 GHz and 18 dBi at 31.5 GHz. The measured efficiency is more than 95% and 80% at sub-6-GHz and mm-wave bands, respectively. At the sub-6-GHz band, the measured 10-dB return loss bandwidth is 33% (centered at 3.6 GHz). At the mm-wave frequency band, the measured 10-dB return loss bandwidth is 27% (centered at 30.5 GHz). The achieved bandwidths are the highest among previous works on dual-band antennas at sub-6-GHz and mm-wave bands.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".