A Multibit and Frequency-Reconfigurable Reflecting Surface for RIS Applications
Bibliographic record
Abstract
Intelligent reflecting surfaces, also known as reconfigurable intelligent surfaces (RISs), have currently received much attention for their applications in wireless communication. The n-bit RISs presented in the literature are restricted to a single resonance and single polarization. These shortcomings create hurdles to engage more number of users and design a low-cost system (i.e., a separate hardware is required for each desired frequency band and each polarization). However, these issues can be solved if frequency reconfigurability and multiple polarization states [i.e., transverse electric (TE) and transverse magnetic (TM)] can be achieved in one structure. This letter presents a new multibit reconfigurable reflecting surface (RRS) with frequency-reconfigurable functionality working for both TE and TM polarized waves. The proposed design is tunable between 1.8–2.3 GHz and 2.95–3.9 GHz for TE and TM polarized electromagnetic waves, respectively. To simultaneously achieve the frequency reconfigurability and phase tailoring (varied from 0$^\circ$to 315$^\circ$) in the proposed structure, the super-cell technique is used. This latter is incorporated in a phase gradient metasurface with different digital coding patterns to perform beam scanning and splitting functionalities. The simultaneous frequency reconfigurability and phase controlling for both TE and TM polarized waves make this RRS a potential candidate for future RIS and reconfigurable devices.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".