Synthesis of Highly Directive Linear Leaky Wave Antennas With Controlled Sidelobe Levels Fed by Nonuniformly Spaced Subarrays
Bibliographic record
Abstract
A synthesis procedure for designing linear Fabry–Perot (FP) leaky wave antennas (LWAs) fed with nonuniformly spaced subarrays is presented. First, the array-fed FP LWA is modeled by the superposition of the excited leaky waves that includes the mutual coupling effects. To that end, the dominant leaky mode and the dispersion curves are calculated using the finite element analysis (FEA) of the eigenmode problem without any homogenization approximations. A procedure is proposed to numerically calculate the self-coupling and mutual-coupling coefficients for the excited leaky waves and include them in the model. Finally, a hybrid method based on convex <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\ell _{1}}$ </tex-math></inline-formula>-norm minimization and particle swarm optimization (PSO) is applied to the model to group the source array elements into a minimum number of subarrays, while fulfilling the predefined mask-type radiation and element location constraints. Two linear FP LWAs are synthesized at 10 GHz to demonstrate the effectiveness of the model and the optimization tool in designing highly directive antennas with controlled sidelobe levels (SLLs); the first achieves a half power beamwidth (HPBW) of 0.95°, aperture efficiency of 83.4%, and SLL of −19.5 dB using three subarrays, while the second achieves an HPBW of 1.05°, aperture efficiency of 82.9%, and SLL of −29.3 dB using nine subarrays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".