High Gain and Wideband High Dense Dielectric Patch Antenna Using FSS Superstrate for Millimeter-Wave Applications
Bibliographic record
Abstract
Gain and bandwidth enhancement of low profile, linearly polarized square dense dielectric patch antennas using a frequency selective surface (FSS) superstrate layer is proposed. A high dense dielectric patch antenna is utilized as a radiating element instead of a metallic patch in order to gain several significant advantages, including low profile, wide bandwidth, and high radiation efficiency. The implemented antenna is excited by an aperture-coupled feeding technique. The antenna gain is enhanced by using a highly reflective FSS superstrate layer, realizing an antenna gain enhancement of 11 dBi. The implemented antenna acquired a measured gain of about 17.78 dBi at 28 GHz with a 9% bandwidth and radiation efficiency of 90%. The bandwidth of the proposed antenna is improved by using a unit cell printed on two sides, as it provides a positive phase gradient over the desired frequency range. The antenna impedance bandwidth is broadened and the measured impedance matching S11 exhibited a 15.54% instead of 9% bandwidth while maintaining a high-gain characteristic of about 15.4 dBi. The implemented antenna presents a solid radiation performance with good agreement between the measured and simulation results. For some attractive advantages such as low profile, low cost, lightweight, small size, and ease of implementation, the proposed antenna is a very good candidate for millimeter-wave wireless communications.
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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".