A Generalized Methodology for Obtaining Antenna Array Surface Current Distributions With Optimum Cross-Correlation Performance for MIMO and Spatial Diversity Applications
Bibliographic record
Abstract
Based on a new formulation of far field cross-correlation involving the currents on the radiating sources, we propose a general methodology employing a Genetic Algorithm (GA) to find optimum distributions of current amplitudes and phases on MIMO antennas such that the resulting system has good cross-correlation (high diversity gain.) The obtained currents can help guide the design and fabrication process of final MIMO antennas by providing valuable information about which current distributions can achieve the best “complementarity” of the individual far fields such that the total diversity gain is maximized. Moreover, this approach will help to explicate what is meant by a `MIMO antenna' from the electromagnetic viewpoint by defining a MIMO antenna as an antenna supporting the optimum currents such as those obtained by the proposed method itself. The method is quite general and can be applied to arbitrary antenna types and array topologies. Verifications for examples comprised of small arrays of half-wavelength dipoles are provided and the practical significance of the results is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".