Bandwidth Enhancement of Low-Profile Metasurface Antenna Using Nonuniform Geometries
Bibliographic record
Abstract
Artificial magnetic conductor metasurface antennas are investigated using a robust, surrogate-assisted, differential evolution optimization technique. Using a uniform metasurface array configuration as a starting point, multiple array configurations are parameterized and the differential evolution optimizer yields nonuniform array geometries exhibiting wideband performance. The bandwidth improvement is attributed to the structure’s ability to support additional higher-order modes with good impedance matching. A comparison between the uniform metasurface antenna cases and their ‘evolved’ nonuniform counterparts is presented to demonstrate the advantages of the proposed technique. Two prototypes are fabricated and characterized to experimentally confirm the advantages of proposed designs. The first prototype, a 3x3 case, measured a fractional bandwidth of 36.8% with peak gain of 8.3 dBi. The second prototype, a 4x4 case, measured a fractional bandwidth of 51.8% with peak gain of 8.8 dBi. When compared to standard uniform cases, the proposed nonuniform geometries exhibit substantial impedance bandwidth enhancement.
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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".