Power Gain Optimization for Multiple Active Multiple Passive Dipole Antenna Arrays
Bibliographic record
Abstract
In this work we consider dipole antenna arrays composed of both active, i.e., excited by voltage sources, and reactively controlled passive dipole elements.Unlike the traditional approach in which the current distribution along the antenna elements is assumed to be sinusoidal, herein we obtain the exact distribution from the Hallen equations using the Method of Moments (MoM).The obtained current distributions are subsequently used to derive exact expressions for the far-field power gain of the antenna array in any given direction.Using the exact current distribution, we show that the current distributions on the dipole elements can deviate significantly from the sinusoidal approximation.Consequently, the exact power gain pattern mismatches that obtained using the sinusoidal assumption for the current distribution.Optimizing the excitation voltages and the load reactances for the active and passive elements, respectively, to maximize the gain; constitutes a non-convex optimization problem.To circumvent this difficulty, we develop an efficient algorithm that yields beam patterns close to those that would be yielded by the all-active analog of the considered antenna array.The advantage of our proposed approach over the one based on sinusoidal approximation of current distributions is demonstrated by numerical evaluation of the derived analytical expressions and verified using the Numerical Electromagnetic Code (NEC) simulator.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".