Analytical and experimental study of gain enhancement in antenna arrays covered with high index metamaterial superstrate
Bibliographic record
Abstract
Abstract Antenna arrays are used in communication applications where directive beams are required.In this article, we show analytically and experimentally that the gain of an antenna array can be further increased by covering it with a high refractive index superstrate. For the experimental demonstration, a corporate‐fed 4 × 1 microstrip patch array is fabricated and the electromagnetic pattern measurements are performed on the array covered with a magneto‐dielectric metamaterial. A fast analytical solution for the radiation fields of the superstrate‐covered microstrip antenna array is also proposed. The analytical solution is based on the reciprocity theorem in conjunction with the transmission line analogy and the cavity model. The artificial superstrate is designed using broadside coupled split ring resonator inclusions and is placed at approximately one‐tenth of a wavelength apart from the patch array operating at 2.2 GHz. A directivity increase of about 3.4 dB is achieved after covering the antenna array with the engineered magnetic superstrate. A comparison of results using the analytical model, the full‐wave simulations and measurements show good agreement. The proposed analytical formulation requires only 2.5% of the time required by full‐wave analysis. The superstrate‐based directivity enhancement method can be used in commercial antennas to combat some of the downsides of the existing systems such as gain decrease due to surface waves and dielectric losses. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:215–218, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27261
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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 | 0.000 |
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 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".