Physical Mechanism and Theoretical Foundation of Ambient RF Power Harvesting Using Zero-Bias Diodes
Bibliographic record
Abstract
Estimating the amount of harvestable ambient RF and microwave power from the omnipresent electromagnetic sources is of vital importance when designing a wireless device that makes use of ambient microwave power harvesting (AMPH) as a power source. This paper studies and looks into the underlying RF and microwave rectification mechanism at low input ambient power levels, specifically -30 dBm and below. A fundamental theory is formulated and developed, which is able to correctly predict the efficiency of a rectifier including the effects of matching network insertion losses through an easy-to-understand analytical model. The suggested model provides a direct design guideline in determining and choosing the optimal diode for a predetermined application. Based on the developed theoretical framework, the diode characteristics that have a direct impact on the microwave power conversion efficiency are discussed in detail. Three different Schottky diode rectifiers were designed on the basis of the tools described in this paper, thereby validating the proposed model and highlighting the influence of critical diode parameters on its performances. The measured results are then compared with those predicted by the proposed model and state-of-the-art microwave power rectifiers, showing a good model accuracy and also a 10% improvement in the rectifying efficiency for the low input power levels of interest.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".