60 GHz 2-D Scanning Multibeam Cavity-Backed Patch Array Fed by Compact SIW Beamforming Network for 5G Applications
Bibliographic record
Abstract
This paper presents a 2 × 2 multibeam array employing a novel wideband linearly polarized cavity-backed patch antenna for fifth-generation (5G) wireless communication technology. The antenna array comprises four layers stacked on top of one another. The proposed antenna element is composed of three substrate layers and excited using a substrate-integrated waveguide aperture-coupled feed. The antenna element exhibits -10 dB impedance bandwidth of 36.2% from 53 to 76.4 GHz with very flat gain and excellent radiation characteristics. A prototype is fabricated and tested. The compact 2-D scanning multibeam 2 × 2 antenna array is demonstrated at 60 GHz, where the proposed antenna element is utilized as a radiating element. By employing a novel compact beamforming network (BFN) in the design, the proposed array achieves a size reduction of more than 28% compared to an array fed with the conventional BFN, without degradation in the array performance. A wide bandwidth larger than 27% for |S11|<; -10 dB and a peak gain of 12.4 dBi is achieved. The array shows a good symmetrical radiation pattern in the two perpendicular planes. Verified by prototype measurements, the proposed antenna element and multibeam array with features of a compact structure, low cost, wide bandwidth, and superior radiation performance would be practically attractive for 5G 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".