An RIS Unit Cell With an Independently Controllable Phase-Frequency Slope and Phase Center
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) has emerged as an important technology to optimize the multi-path environment in the next generation of wireless communication systems. RIS is typically a 2D array of reconfigurable unit cells that forms a controllable beam by creating a phase gradient across the array surface. In this work, we design an electrically tunable linearly polarized RIS at 2.5 GHz that yields a resonance profile with both a controllable phase at the band-center frequency and a controllable phase-frequency slope. In other words, we add the tunability of the phase-frequency slope to the tunability of a resonance center frequency to increase the achievable rate of wideband communication systems. The proposed design consists of three layers of unit cells with dog-bone-shaped elements in the top layer, patch elements in the middle layer, and a ground plane in the bottom layer. Each patch and dog-bone-shaped element is loaded with a varactor. The reverse bias voltages are then controlled to provide a phase-frequency profile with +90°, 0°, and −90° phase shift at 2.5 GHz and a slope that varies between 0.39°/MHz and 6.56°/MHz. Measurements at 2.5 GHz using a prototype array of 4 × 5 dog-bone elements overtop of 5 × 5 patch elements show phase-frequency profiles with +90°, 0°, and −90° phase shift and slope values between 0.42°/MHz and 9.35°/MHz, thus validating the design.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".