Two-Dimensional Beam Steering Field-Programmable Digital Coded Metantenna for Intelligent Reflecting Surface Applications
Bibliographic record
Abstract
This study presents a novel low-profile 1-bit metantenna based on a unique microwave resonator, optimized for sub-6 GHz frequencies. The resonator features four symmetrically connected quartiles around a central H-shaped microstrip, forming a compact meta-atom with four-fold mirror symmetry. This design is ideal for miniaturization and multi-frequency applications, making it suitable for 6 G wireless communication systems. A single p-i-n diode is loaded in the meta-atom, provides electronic control for 2D reflected beam steering. The metantenna is designed on a single-layer substrate, with the resonator and bias lines on the top layer and a metallic ground plane at the bottom. A metallic shorting via connects the top layer to the ground, providing a negative bias for the diode. The surface impedance is modeled as a lumped parallel resonant circuit. The metantenna achieves two switchable states with a phase difference of 180° and a low reflection loss. MATLAB optimization improves beam steering performance through feed location and aperture phase distribution. Full-wave simulations and theoretical results demonstrate efficient 2-D beam steering of$\pm 40^{\circ}$with a high aperture efficiency of 20 % at$\mathbf{3. 9 ~ G H z}$in both the horizontal and vertical planes, along with a consistent gain of 13 dB.
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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) | 0.001 | 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".