A Low Pulling Effect Self-Isolated Harmonic Active Radiator for Millimeter-Wave Internet-of-Things Applications
Bibliographic record
Abstract
A low pulling effect self-isolated harmonic active radiator operating around 28 GHz is proposed, investigated, and demonstrated in this article. Thanks to the inherent self-isolation property of a feedback loop of the proposed active radiator, a superiority of pulling effect mitigation, harmonic output, and simple structure is achieved. As part of the loop, a partially air-filled dual-mode substrate integrated waveguide cavity with a dielectric-loaded broadside slot is designed to realize the functions of resonators, harmonic diplexers, and antennas concurrently. The radiator oscillates at a fundamental frequency which is set by the first mode of the cavity resonator, and the generated second harmonic signal is extracted and radiated by the diplexer and antenna functions, respectively. Self-isolation is achieved because a pulling signal is attenuated by the synergetic effect of the reverse isolation and harmonic separation of the loop amplifier and diplexer, respectively. Measured results of a fabricated prototype show a maximum equivalent isotropic radiated power of 15 dBm at 28.15 GHz and a phase noise of -108 dBc/Hz at 1 MHz offset. Load pulling and injection pulling are reduced by a factor of 10 and 17, respectively, compared with an active radiator transmitting a fundamental signal around 28 GHz. Such a robust and compactly integrated structure is a good candidate for low-cost millimeter-wave Internet-of-Things applications in connection with identification, sensing, tracking, and communication.
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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 | 0.000 |
| 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) | 0.001 | 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".