A Scalable Densely Packed Configuration for Continuous OAM Beam Steering With Uniform Circular Self-Filtering Antenna Array
Bibliographic record
Abstract
This paper presents an advanced densely packed configuration of a Uniform Circular Self-Filtering Antenna (UCSFA) array, featuring an innovative two-layer architecture that seamlessly integrates Reflection-Type Phase Shifters (RTPS), an edge-fed eight-way radial power divider, and self-filtering antennas. The RTPS are designed to provide continuous 360-degree phase shifts with minimal insertion loss variation across different phase states. These phase shifters can be independently tuned using voltage controllers, enabling precise generation and direction of the Orbital Angular Momentum (OAM) beam. Moreover, we proposed a self-filtering antenna that operates without the need for supplementary circuitry, showcasing a band-pass filter response across its operational frequency range by adjusting the dominant mode’s electric field distribution. This antenna offers a broader bandwidth and achieves a significant 15 dB reduction in cross-polarization compared to conventional rectangular patch antennas (RPAs). The filtering capabilities effectively mitigate unwanted harmonics, noise figure (NF), and losses within wireless communication systems. Finally, a prototype of the proposed configuration has been fabricated, demonstrating commendable alignment between simulation results and experimental measurements, thereby enhancing the efficiency of the OAM-based wireless communication link.
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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".