Novel Compact Microstrip Antennas With Two Different bands For 5G Applications
Bibliographic record
Abstract
In this paper, millimeter-wave (MMW) antennas with two different designs have been proposed for fifth generation (5G) wireless applications. These novel antennas have a greater fractional bandwidth and an appropriate gain, making them suitable for a variety of applications such as fixed wireless services, broadcasting, land mobile, and many millimeter applications. Each antenna design has a different model and characteristics. The two designs resonate at different frequencies, 39.7 GHz and 43 GHz, which are suitable for 5G applications in many countries like Canada, the United States of America, Japan, and Australia. A commercial electromagnetic simulator (CST-Studio) was used to construct and optimize the proposed models. The proposed MMW models are designed on a compact Rogers Substrate RT-5880 with a thickness of h = 0.508 mm, a loss tangent $\delta$ value of 0.0009, and a dielectric constant $\left(\varepsilon_{r}\right)$of 2.2. The proposed antennas have a simple design structure to ensure reliability, mobility, and high efficiency, which can be used for many (5G) wireless applications. The proposed models provide a moderate gain of 6 dBi to 7 dBi. The impedance bandwidth of the proposed models ranges from 38.4 GHz to 41.1 GHz for the first model, which is equal to 2.7 GHz, and 41.6 GHz to 44.7 GHz for the second model, which is equal to 3.1 GHz. The efficiency of the models is about 73.8% and 78%, respectively, which is sufficient to meet the requirements of 5G wireless applications.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".