Millimeter-Wave Yagi MIMO Antenna With High Isolation and Beam-Tilting Capability Using Optimized Metamaterials
Bibliographic record
Abstract
A high-performance multiple-input–multiple-output (MIMO) antenna for the millimeter-wave spectrum is introduced in this paper, featuring broadband performance, high gain, superior isolation, and beam-tilting functionality. A single Yagi antenna, utilizing a third-order mode dipole as its driver with integrated reflectors, is designed for the 28 GHz band to enhance gain. The rigorous numerical optimization procedure was employed to precisely adjust the dimensions and positions of the driven dipole, director, and reflectors. The optimized Yagi antenna functions within the 28 GHz 5G band, offering a 2.6 GHz bandwidth. The radiation pattern results reveal that the antenna’s gain in this higher resonant mode exceeds that of a conventional Yagi-Uda antenna, achieving a gain of 9.35 dBi at 28 GHz. The MIMO antenna is subsequently designed using two adjacent Yagi elements, with a metamaterial array positioned between them to enhance isolation and tilt the radiation beam. The trust region (TR) method is utilized to refine the metamaterial’s dimensions, achieving up to 58 dB of isolation at 28 GHz. Additionally, the metamaterial enables a 33-degree E-plane beam tilting relative to the end-fire direction when switching between the two ports’ excitations. The system is validated through experiments, demonstrating a strong correlation between the simulated and measured data.
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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) | 0.000 | 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".