Beam-Steering in Super-Directive Antenna Arrays Using Loaded Parasitic Elements
Bibliographic record
Abstract
Super-directive antenna arrays (SDAAs) suffer from low realized gain caused by high impedance mismatches and low radiation efficiencies. Additionally, since the maximum radiation is directed only towards the end-fire, the practical applications of SDAAs have been historically restricted due to their limited coverage area. When classical phase shifters are employed for beam-steering, there is a significant decrease in the gain attributed to the amplified sensitivity of the realized gain to antenna feed currents. Therefore, innovative beamforming network (BFN) configurations are required to direct the beams while maintaining high realized gain in SDAAs. To address this fundamental challenge, we introduce a novel configuration employing parasitically loaded elements for beam-steering applications within a four-element array, aiming to achieve high realized gain. Firstly, the effect of the parasitic elements’ positions on the array’s beam-steering performance is examined. Subsequently, a reconfigurable BFN is introduced, incorporating adjustable passive circuits within the parasitic elements, which remain in fixed positions. The configurations obtained through multi-parameter optimization are analyzed in full-wave electromagnetic simulation software, and the results are presented comparatively. The common belief that constrains the practical applications of SDAAs – that "maximum radiation is directed towards end-fire" – is challenged as a result. Testament to this, we achieve a 34% increase in gains compared to a uniform uncoupled array for the final loaded configuration, augmented with the ability to steer the beam towards any desired direction.
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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".