Wide-Angle Beam-Steering and Adaptive Impedance Matching With Reconfigurable Nonlocal Leaky-Wave Antenna
Bibliographic record
Abstract
We present a simple reconfigurable leaky-wave antenna (LWA) capable of fixed-frequency continuous beam-scanning, along with an efficient optimization procedure with which its radiation pattern can be shaped. To construct the LWA, an array of varactor-loaded unit-cells are connected in series over a grounded dielectric substrate, forming a reconfigurable leaky microstrip. The varactors are individually addressed, with a biasing scheme determined via optimization. To ensure the practicality of the converged solutions, several feasibility-based optimization constraints are derived. In contrast with conventional LWAs, the proposed device is synthesized based on an aperiodic non-local model which rigorously accounts for the mutual coupling between all radiating elements. As a result, it can be optimized to meet very demanding requirements, such as wide-angle continuous beam-scanning with strong side lobe suppression. Importantly, the antenna input impedance can be adaptively tuned to match that of the source, without the need for a complicated matching network. Furthermore, the optimization procedure can accommodate different end terminations, even when they cause strong backward reflections. These features of the proposed LWA are confirmed through numerical simulations. Measurements of a fabricated prototype working at 10 GHz demonstrated a 130° continuous scan range, and a peak gain of 8.9 dBi.
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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 | 0.000 |
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".