Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays
Bibliographic record
Abstract
We propose a novel approach for boosting the realized gain of arrays with enhanced directivity, utilizing both active and parasitic dipoles. The optimization process first maximizes the end-fire gain in the active array by selecting the optimal current excitation vector. For the parasitic arrays, the dipoles are reactively loaded based on the input impedances of the active dipoles, after which the optimization focuses on the inter-element distance to achieve a balance between the gain and the reflection efficiency. This multi-objective optimization, underpinned by the differential evolution (DE) algorithm, uses a simple wire dipole as the unit element. Full-wave simulations validate our theoretical results, showing that our two- and three-element parasitic arrays achieve realized gain comparable to state-of-the-art designs without relying on intricate unit elements or resource-intensive simulations, while our four- and five-element parasitic arrays yield the highest realized gain values reported in the literature. The simplicity of our approach allows optimizations to run significantly faster than full-wave simulations, whilst the sensitivity analysis showcases the robustness of the design under small deviations in loads and element positioning. Compact and power-efficient, the proposed parasitic arrays are well-suited for base stations, aligning with modern communication system requirements while minimizing hardware complexity.
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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".