A Design Framework for Reconfigurable Intelligent Metasurfaces Enabling Full-Space Beamforming Using Auxiliary Surface Waves
Bibliographic record
Abstract
Electromagnetic metasurfaces (MTSs) and Reconfigurable Intelligent Surfaces (RISs) have revolutionized wave manipulation by enabling precise control over the reflected and transmitted waves. While traditional MTSs rely on static designs, RISs incorporate tunable elements, allowing dynamic adaptation to external illuminations for real-time wavefront optimization. This paper provides a complete design framework for RISs based on the Method of Moments (MoM) to establish a multiport network and an efficient optimization scheme. By incorporating auxiliary surface waves and accounting for mutual coupling, precise control over amplitude and phase can be achieved. As a result, our designs enable wide-angle beamsteering of up to 70° in a single plane, as well as out-of-plane anomalous reflection. To further demonstrate the capabilities of the design framework for complex wave manipulation, a Chebyshev pattern with a -20 dB sidelobe level, as well as sector patterns in a single plane and across a hemisphere, are achieved using the designed RIS. The effectiveness of the proposed method is validated through full-wave simulations, supplemented by an additional measurement of a static MTS exhibiting out-of-plane anomalous reflection.
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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 | 0.000 | 0.000 |
| 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".