Guided-Wave-Excited Binary Huygens’ Metasurfaces for Dynamic Radiated-Beam Shaping with Independent Gain and Scan-Angle Control
Bibliographic record
Abstract
This paper presents a reconfigurable metasurface that is able to dynamically and independently control the gain and propagation direction of the radiated field, with reduced biasing complexity and power consumption at a low profile. Moreover, the proposed metasurface is guided-wave fed thus leading to a compact tunable leaky-waveguide structure. Heretofore, reported reconfigurable metasurfaces have mainly demonstrated dynamic tailoring of free-space waves by redistributing their reflection or transmission phase profiles at a fixed amplitude. In contrast, we demonstrate dynamic transformation of a guided wave into an aperture field with controlled amplitude such that the gain and scan angle of the corresponding radiated field are dynamically and independently controlled. In particular, the aperture field is digitally synthesized by the proposed tunable Huygens' metasurface whose local transmission coefficient is able to be dynamically tuned as two digital bits of $\ensuremath{-}|{T}_{o}|$ and $+|{T}_{o}|$, where ${T}_{o}$ represents a user-defined constant. This digital synthesis and the deliberate utilization of a nonbianisotropic type Huygens' metasurface simplify biasing requirements, and make the proposed design more feasible. Through simulations and experiments, we show dynamic steering of a beam from $\ensuremath{-}{40}^{\ensuremath{\circ}}$ to $+{40}^{\ensuremath{\circ}}$, and two broadside radiations with different radiation gains at a fixed operating frequency of 5 GHz.
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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".