Low-Power 2D Reconfigurable Reflecting Surface With High-Speed Serial Control for Continuous Scanning and Tracking
Bibliographic record
Abstract
This paper presents a complete design framework for a low-power, fast-switching Reconfigurable Intelligent Surface (RIS) operating at 5.2 GHz, with capabilities for sidelobe shaping, intelligent tracking, and signal scanning. The framework centers on the design methodology and implementation of a high-frequency, low-power, low-complexity serial control circuit tailored for varactor-based tuning. A custom RIS prototype was developed to demonstrate the circuit’s ability to assign arbitrary voltages across a 64-element array, enabling reconfiguration at 60 Hz while maintaining an average power consumption of only 39 mW. To validate the practicality of the system as an intelligent platform, two key functionalities are demonstrated: (1) intelligent tracking of a moving transmitter to maintain optimal signal strength during video transmission, and (2) spatial signal scanning to map the distribution of signal strength across both elevation and azimuthal planes. Finally, both simulated and measured performance results, including beam patterns, bandwidth, and other relevant metrics, are presented to validate the effectiveness of the proposed RIS system.
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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) | 0.002 | 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".