AI-Empowered mm-Wave Antenna Design
Bibliographic record
Abstract
The performance of wireless 5G communication networks can be significantly enhanced by integrating compact patch antenna systems with machine learning (ML) techniques. In this study, a compact antenna is proposed, constructed on a Rogers 5880 substrate, making it highly suitable for high band 5G applications. The antenna demonstrates excellent performance, an impedance bandwidth ranging from 26.13 GHz to 26.18 GHz within the −10 dB reflection coefficient range. Despite its compact dimensions$\left(5.33 \times 6.3 \times 0.13 \text{mm}^{3}\right)$, the antenna achieves an efficiency of around 89%. CST software is used to compare the return loss characteristics with the HFSS generated model of the proposed microstrip patch antenna (MPA). Following this, extensive data sampling is carried out using HFSS, and regression techniques are applied for performance prediction. Among the tested ML methods, the Multi-Layer Perceptron (MLP) Regressor the most accurate results, demonstrating the lowest prediction error, particularly in bandwidth estimation. Overall, the proposed antenna proves to be a strong candidate for high frequency 5 G communication systems. Designing a compact patch antenna for 26 GHz mm-Wave 5G applications presents considerable challenges, primarily in achieving performance for mm-Wave 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".