High-Efficiency Class-E Seamlessly Integrated Active-Integrated Antennas of Far-Field SWIPT Base Stations for Batteryless IoT Applications
Bibliographic record
Abstract
A deep integration of active-integrated antennas (AIAs) with high transmitter efficiency (TE), high polarization purity, and low interference has presented a difficult-to-ignore challenge for designers. This fundamental problem in AIA design emerges from the lack of an impedance-matching flexibility between the antenna aperture and active element for high dc-to-RF efficiency without deteriorating the radiator’s performance. This work proposes and presents an approach for the seamless integration of AIAs based on a modified rectangular patch loaded with shorting pins. Numerical and experimental results obtained in this research demonstrate that by distributing the shorting pins in the nodes of the fundamental and second-harmonic spatial modes of the canonical patch, new degrees of freedom for flexible impedance matching can be achieved without compromising the radiator’s radiation pattern, polarization purity, and radiation efficiency. The effectiveness of the proposed design strategy is illustrated by a fabricated seamlessly integrated AIA that presents the highest active gain reported in the literature while keeping cross-polarization (CP) and harmonic-interference levels low. Based on the obtained results, the proposed design strategy is believed to have great potential for developing low-cost and high-efficiency far-field simultaneous wireless information and power transmission (SWIPT) base stations for batteryless Internet of Things (IoT) applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".