Compact High Gain Microstrip Array Antenna Using DGS Structure for 5G Applications
Bibliographic record
Abstract
In this paper, a microstrip millimeter-wave (MMW) array antenna with a defected ground structure (DGS) has been presented for the applications of fifth generation (5G) wireless networks. This novel antenna, which has small dimensions with higher gain, can be used for licensed 5G applications in many countries, like the United States of America, Canada, Australia, Japan, India, and China. It also covers a band that is planned for licensed use in some countries, like Colombia and Mexico. The proposed model has a single element design, and for gain and efficiency enhancement, a two-element array has been designed. Both single and two element models resonate at a frequency of 39.96 GHz. Using a commercial electromagnetic simulator (CST-Studio), the model was designed and optimized with the goal of achieving a return loss rate of less than -10 dB. The proposed antenna is built on a compact Rogers substrate (RT-5880) with dimensions of 6 mm 6 mm for the substrate of the single element and 9 mm 13 mm for the two-element array. The substrate has a thickness of 0.508 mm, a dielectric constant r of 2.2, and a loss tangent tan value of 0.0009. This suggested design is small, low profile, and simple to guarantee the dependability, mobility, and high efficiency needed to be used with a variety of 5G wireless applications. The high gain of 11.6 dBi for the two-element array model of the proposed antenna is one of its distinctive features. The suggested single element model has an impedance bandwidth of 2.3 GHz, and 2.1 for the two-element array model, satisfying efficiency of approximately 73.5% for the single element and 85% for the two-element array model, respectively. The proposed structure, compared to other designs found in the literature, has smaller size while maintaining other parameter values of comparable orders.
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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 | 0.001 |
| 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".