A Systematic Approach for Mutual Coupling Reduction Between Microstrip Antennas Using Pixelization and Binary Optimization
Bibliographic record
Abstract
This letter introduces a systematic procedure to reduce the mutual coupling (MC) between radiating elements in a microstrip array antenna. The proposed method is based on using parasitic elements between the radiating elements. The shape of the parasitic element, which impacts the MC and other radiation properties of the antenna, is determined in a systematic pattern optimization procedure based on pixelization of the area between the antennas and application of a binary optimization algorithm. A bit with binary values of 1 or 0 is assigned to each pixel, which signifies the presence or absence of copper on the pixel surface, respectively. Afterwards, during a binary particle swarm optimization algorithm, the optimal value of each bit is obtained, which results in the optimum shape of the parasitic element. In the optimization process, reducing the MC between the radiating elements is set as the main optimization objective while maintaining good antenna impedance matching and preserving of the radiation pattern. Using the proposed method, we designed a parasitic decoupling element (resonator) between two microstrip patch antennas, which were placed close to each other. The results demonstrate that adding this resonator between the two antennas fulfilled the design objectives and reduced the MC between radiating elements by 24 dB, while preserving the radiation pattern and impedance matching. The results of the simulations have been verified by fabrication and measurements.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".