Efficient Procedure to Design Large Finite Array and Its Feeding Network With Examples of ME-Dipole Array and Microstrip Ridge Gap Waveguide Feed
Bibliographic record
Abstract
An efficient procedure to design a large planar array and its corporate feeding network is presented. The procedure is verified by 8 × 8 and 16 × 16 arrays of magnetoelectric dipoles (ME dipole) fed by microstrip ridge gap waveguide (MRGW) through a narrow slot. This procedure is based on using the frequency-dependent effective input impedance at the port of each element, which includes the effect of the mutual coupling between the antenna elements, which is used to design the corporate feeding network. In addition, the far-field characteristics of the array parameters such as directivity, gain, and the radiation patterns are predicted using the pattern multiplication method including the mutual coupling. The results are verified with the full-wave numerical solution. The procedure requires limited resources and speed up the design cycle. In the presented examples, the array elements are directly excited by the MRGW, which provides more flexibility to design complicated feeding networks and allow for distances between the elements less than a wavelength. Therefore, grating lobes are avoided. To accommodate such constraints, special designs of the power dividers are performed to provide the symmetric location of MRGW lines to avoid coupling between the feeding network lines. Furthermore, a transition from waveguide WR-15 to the MRGW is proposed to differential feed of the array antenna. The 16 × 16 array of ME dipoles has been fabricated. The measurement results show a 19% matching bandwidth (|S11| <; -10 dB) and measured gain above 30 dBi with radiation efficiency better than 71%, all within a common bandwidth of 56-66 GHz.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".