High-Gain Wideband Circularly Polarised Fabry–Perot Resonator Array Antenna Using a Single-Layered Pixelated PRS for Millimetre-Wave Applications
Bibliographic record
Abstract
In this paper, a wideband and high-gain circular polarised Fabry-Perot Resonator Antenna (FPRA) with a single partially reflective surface (PRS) layer is automatically generated and optimised using a VBA-based interface system between CST Microwave studio and Matlab. The proposed PRS layer is a promising superstrate for wideband and high-gain FP resonator antennas due to its relatively high reflection coefficient magnitude and positive phase gradient, which resemble that of the optimum PRS over the relevant frequency band. The circular polarisation was achieved using a sequential feeding network for a 2 × 2 array air-gapped slot-coupled elliptical patch antenna. The proposed design achieved an impedance bandwidth of 48.58% (15.3 GHz) ranging from 23.84 GHz to 39.14 GHz, and the -3 dB gain bandwidth was 22.42% (6.25 GHz) from 24.75 to 31 GHz, with a peak gain of 17.12 dB at 29 GHz, and an axial ratio bandwidth of 21.75% (6.2 GHz). In addition, the achieved radiation efficiency was 90%. Consistent and almost invariant radiation patterns are achieved over the millimetre-wave frequency band of interest. The experimental and simulated results are in good agreement, justifying the feasibility of the proposed design as a high-gain and wideband FP resonator array antenna for Mm-wave applications.
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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) | 0.001 | 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 | 0.001 |
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".