A Versatile RF Energy Harvester for IoT Sensors in 5G Network With Extended Input Power Range
Bibliographic record
Abstract
The widespread adoption of fifth-generation (5G) wireless technology in Internet of Things (IoT) networks introduces a battery replacement challenge for countless IoT sensors. To address this issue, researchers are exploring energy harvesting techniques that involve extracting energy from radio frequency (RF) signals. The dense deployment of antennas in 5G networks, compared to prior technologies, ensures that an RF energy source is available in close proximity to IoT sensors which can be utilized as reliable power sources for these sensors. This study focuses on the design of an efficient energy harvester for low-power IoT sensors. Specifically, the investigation centers around a scenario where dedicated directional transmitters emit continuous waves to power up IoT sensors. An optimized high-efficiency Dickson rectifier circuit is designed to convert the RF signal to DC at a high-frequency band over a wide dynamic range. The Dickson rectifier is superior for low-power, high-frequency scenarios due to its voltage multiplication and reduced losses. To minimize the effect of parasitic capacitance associated with conventional capacitors, interdigital capacitors (IDC) are designed on an FR-4 substrate with a thickness of 1.57 mm. An impedance-matching circuit utilized as a passive voltage amplifier was also employed to enhance the RF-to-DC conversion efficiency. A prototype was built and tested at the 5.2 GHz frequency band. The measurement results demonstrate the peak efficiency of the proposed energy harvesting circuit which was determined to be 73.46% when subjected to an input power of -5 dBm at$500~\Omega $load and 49.12% with -10 dBm input at 1 M$\Omega $. The proposed energy harvesting solution can operate efficiently across a broad dynamic range, outperforming the solutions reported in the literature.
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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 | 0.000 | 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".