Performance Analysis of Multistage Voltage Doubler Rectifier for RF Energy Harvesting
Bibliographic record
Abstract
The conversion of electromagnetic energy into DC electrical energy is increasingly considered as the most appropriate solution to overcome the energy dependence of wireless sensor nodes (WSN). However, due to RF exposure limits, the output voltage levels reached are generally low. To amplify this voltage, the use of multi-stage voltage doubler (MSVD) rectifiers is the solution proposed by most designers. Nevertheless, it is recalled that in addition to the supply voltage, the effective of a WSN also depends on the harvested power by the conversion circuit; this issue is not very often dealt with in the design of the MSVDs. In this paper, it is proposed an analysis of the MSVDs to define how beneficial it to amplify the DC voltage without degrading the output power level of the conversion circuit. To consider these two performance criteria, a Rectifier Figure of Merit (RFoM) is defined. The results obtained through simulations with the Advanced Design System (ADS) software demonstrate the existence of an optimum number of stages that is linked to the RF power level incident. As a result of this observation, a relationship between the incident RF power and the optimal number of stages of the MSVD is established in this paper.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".