A Rectenna Design With Quasi-Full Spatial Coverage Based on a Compact Dielectric Resonator Antenna
Bibliographic record
Abstract
In this article, a rectenna with quasi-full spatial coverage is developed for RF power harvesting applications in the 5G context. The rectenna is designed based on a compact dielectric resonator antenna (DRA) that is composed of two laterally radiating elements with opposite half-space radiation. To demonstrate the rectenna’s performance, the proposed DRA is integrated with an efficient rectifier circuit based on the Schottky diode. The whole rectenna is verified at a 3.5 GHz-frequency band. The proposed DRA exhibits a measured impedance bandwidth of 19.43% (3.02–3.7 GHz), while maintaining a small size of$0.57\lambda _{0} \times 0.57\lambda _{0} \times 0.13\lambda _{0}$. At 3.5 GHz, the single element of the DRA can present measured half-power beamwidths of 200° and 156° in xy- and xz-plane, respectively. The rectifier circuit was also measured showing the highest efficiency of 76.6%. Meanwhile, it can maintain a high efficiency of >50% in the power range of 1–14 dBm over the entire frequency band of 3.4–3.6 GHz. Finally, the RF power collecting ability of the proposed rectenna is verified by rotating the rectenna in two different planes. The measured outputs of the rectenna are found to be consistent with expected values.
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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) | 0.000 | 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 | 0.001 |
| 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".