A novel SW-ESIW slot antenna and its applications in millimeter-wave array design
Bibliographic record
Abstract
A novel structure of slot antenna with high gain and compact size for millimeter-wave (mmW) applications is proposed. This is the first attempt to apply slow-wave substrate integrated waveguide (SW-ESIW) to the field of antennas and their arrays. Two slot antenna arrays were designed, fabricated and measured. The measured results are in agreement with the simulation. The removal of dielectric substrate improves the radiation gain. At the same time, the slow wave effect, by means of physical separation of electric and magnetic fields, decreases both the lateral and longitudinal dimensions of the antenna. The SW-ESIW slot antenna can achieve miniaturization while retaining the advantages of high gain of ESIW. The 1 × 4-slot array shows a measured −10 dB bandwidth of 16% (30.5–35.8 GHz) with a measured maximum gain of 11.69 dB. The 4 × 4-slot array achieves a measured −10 dB bandwidth of 7.6% from 31.6 to 34.1 GHz with a measured peak gain of 18.75 dB. In addition, during the operating band, the radiation pattern is stable. Compared with the previously structures, the proposed SW-ESIW antenna structure has a better trade-off between gain and footprint. It can be flexibly adjusted to adapt to different requirements. These performances ensure that the antenna array based on the proposed structure is a promising candidate for millimeter-wave wireless applications including the fifth-generation mobile communications.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".