Beam-Switching Antenna Using Reconfigurable Intelligent Frequency Selective Surfaces for Internet of Things Applications
Bibliographic record
Abstract
Beam Steering antennas play an essential role in mobile applications, including Internet of Things (IoT) devices, due to their capability to control the beam direction, reduce the multipath problem, and enhance the spectrum efficiency. In IoT applications, a large number of devices are deployed over a wide area, and efficient wireless communication is critical to ensure reliable data transfer. The intelligent beam steering antenna technology can be used to dynamically control the radiation pattern of the antenna, providing improved signal quality and increased network capacity. This can result in reduced power consumption and increased battery life for IoT devices. This article presents a novel beam-switching antenna based on reconfigurable intelligent frequency selective surfaces (RI-FSSs) offering a wide steering angle. To cover the complete 360° range at 2.45 GHz, both mechanical and electrical switching are involved. Three frequency selective surface (FSS) panels based on periodic unit cells are arranged in such a way resembling a parabolic reflector. To achieve the required mechanical rotation angle, programmable step motors are installed. The switching between transmission and reflection is attained by varying the biasing of the PIN diodes through a programmable microcontroller. Both mechanical and electrical reconfigurability result in a beam-switching antenna system, having the capability to offer a continuous beam scanning range of 360° by using various combinations of FSS diode ON/OFF states, and the panels’ rotation angles. The proposed beam-switching antenna system is fabricated. The simulation and measurement results are in good agreement, which validates the operating principle of the proposed beam-switching antenna system. The FSS panels’ dimensions are$0.13\lambda \,\times \, 0.1\lambda $, where the overall size of the proposed antenna system is$1.95\lambda \,\times \, 1.50\lambda $. A beam scanning range of 360° with a gain of 10 ± 1 dB makes this a suitable candidate for IoT applications.
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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".