A metasurface assisted pin loaded antenna for high gain millimeter wave systems
Bibliographic record
Abstract
High gain antennas play an essential role in future wireless applications by ensuring sufficient compensation for propagation losses in long-range communication. This work presents a novel design of metasurface-inspired high gain miniaturized antenna for 28 GHz millimeter wave applications. Firstly, to improve the radiation gain of the suggested antenna, only two metallic shorting pins are inserted between radiating patch and ground of antenna.The total size of the proposed shorting pin based microstrip patch antenna is 18 $$\times$$ 14 mm2. This approach differs from traditional methods of increasing the radiating area or using bulky techniques. For further gain enhancement, the antenna incorporates a single layer of metasurface made up of a compact and uniquely designed reflecting metamaterial unit cell array. The distinctiveness of these unit cell design is centered on a simple 2 $$\times$$ 2 array created by combining circular and hexagonal split rings. This innovative configuration improves antenna gain while maintaining a compact form factor.This metamaterial array is positioned at approximately 0.5 $$\lambda _{0}$$ (where $$\lambda _{0}$$ is the wavelength in millimeters) above the antenna, thus creating a Fabry-Perot cavity, and substantially improves the gain exceeding 13 dBi.The metasurface, which is made up of specially designed unit cells, modifies the electromagnetic field distribution and surface waves to produce better radiation properties. Furthermore, the final optimized bandwidth achieved is 26.62–29.79 GHz with peak radiation efficiency up to 94%, respectively. The findings provide satisfactory agreement when comparing simulation results with experimental data. The presented antenna system is developed to meet stringent gain requirements for 5G antennas operating in the millimeter wave frequency band.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".