Metamaterial for gain enhancement of printed antennas: Theory, measurements and optimization
Bibliographic record
Abstract
Metamaterials have been shown to enhance specific performance parameters of low profile and high-profile antennas. Our focus in this paper on specifically increasing the gain of low-profile antennas and in particular the microstrip patch antenna. By placing a metamaterial slab above a microstrip patch antenna (as a superstrate), we show that the gain of the antenna can be enhanced appreciably. The key advantage of using the superstrate is to maintain the low-profile advantage of microstrip patch antennas. In previous works, different types of superstrates were proposed to enhance the gain of microstrip antennas, however, to the best of our knowledge, no theory was developed to understand the mechanism behind the enhancement in the gain. In this paper, we present a simple analytical formulation that provides a very accurate prediction of the gain when a superstrate is used. In fact, our analytical technique is capable of predicting the gain when a multilayer superstrate structures is used. To validate the theory of gain enhancement, antennas and superstrates using metamaterials were fabricated and tested in an echoic chamber. The metamaterials developed were based on split-ring resonators. Strong agreement was found between the measurements and full-wave simulation using commercial tools. Finally, we present optimization results to demonstrate the maximum gain enhancement potential that can be achieved when superstrates are used.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".