Optimum Design of a Beam-Forming Array of S-Shaped DRA Elements With a Superstrate on an SIW Feed for 5G Mobile Systems
Bibliographic record
Abstract
In this letter, an array of dielectric resonator antennas (DRAs) is proposed for the fifth-generation mobile network. A novel S-shaped DRA is presented as array elements. The proposed configuration is achieved by making two symmetrical segments on a conventional rectangular DRA. The results show that in this configuration the higher order modes of antenna are excited, and therefore, the 3 dB gain bandwidth and impedance bandwidth of antenna are increased. The volume of the proposed DRA without ground plane is 0.38 λg3. The proposed DRA provides maximum bandwidth and gain per unit volume compared to similar designs in the literature. Furthermore, the benefit of a mutual coupling (MC) compensation approach in a linear DRA array is investigated. The results show that the accuracy of a computed MC matrix is better at the resonance frequency relative to other frequencies. Also, the performance of a beam-forming algorithm is improved by using this matrix for compensating the MC effects. The proposed array is fabricated and its characteristics are measured. A very good agreement between the numerical and measured results is obtained.
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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 | 0.000 |
| 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) | 0.002 | 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".