Dielectric Resonator Antenna Design for 5G mm-Wave Band
Bibliographic record
Abstract
This work presents a design methodology for the development of a dielectric resonator antenna (DRA), which is optimized for the 28 GHz band, targeting advanced 5G and 6G applications. Traditional antenna designs, such as those for monopole and dipole antennas, face limitations at mmWave frequencies due to high conduction losses. In contrast, DRAs offer improved radiation efficiencies, wider bandwidths, and reduced losses, making them ideal for next-generation wireless systems. This paper focuses on optimizing DRAs by using a microstrip lineslot coupling feed, guided by analytical formulas and enhanced through deep neural networks (DNNs) for predicting key design parameters. The proposed antenna achieves a bandwidth of 8.5 GHz, covering the 28 GHz frequency band, and demonstrates a peak gain of 8.4 dBi, outperforming conventional single-element patch antennas. A genetic algorithm (GA) optimizes the design further, enhancing both bandwidth and gain for superior performance.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".