Efficient Wide-Power-Range RF Power Harvesting System for Self-Sustainable Wireless Sensor Nodes: IEEE ICMMT 2025
Bibliographic record
Abstract
This paper proposes an efficient wide-power range radio frequency (RF) power harvester to address the challenge of reduced power conversion efficiency (PCE) in wireless power harvesting systems (WPHS). To manage the variable load conditions introduced by a DC-DC boost converter, a power partitioning network within the power harvester was designed. This network automatically adjusts the power ratio between two branches of the power harvester at different input power levels. As a result, a wide dynamic power range with high PCEs is realized, while showing a stable output voltage of 2.8 V. The proposed power harvester demonstrates a measured PCE larger than 70% at 2.45 GHz over an input power range of 2.7-13.3 dBm. It successfully charges a super capacitor to 3.3 V, forming a reliable WPHS with wide dynamic power range. Finally, a totally self-sustainable wireless sensor node based on this system has been demonstrated.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".