A High-Gain Microstrip RF Energy Harvester for IoT Sensors in 5G Network
Bibliographic record
Abstract
As the implementation of fifth-generation (5G) wireless networks expands, the interest in using radio frequency energy harvesting (RFEH) as a power source for low-power Internet of Things (IoT) devices increases. RFEH has the potential to eliminate the need for batteries, providing a reliable and sustainable power source for these devices. However, the efficiency of RF to dc converters at high frequencies, commonly used in 5G networks, is relatively low. This article proposes a new high-gain RFEH circuit that incorporates a$2 \times 2$patch array antenna, a reflector, and a cascade microstrip L-matching network used as a passive voltage amplifier to increase the induced voltage across the antenna. The RFEH receiver is fabricated on a low-cost FR-4 substrate with a dielectric constant of 4.6 and a copper thickness of 0.035 mm. To enhance efficiency in RF to dc conversion, a novel microstrip voltage amplifier is designed, resulting in an overall gain of 30 dB at an input power of -30 dBm with a 1-M$\Omega $load. This amplifier exclusively utilizes traces on the printed circuit board (PCB), eliminating the need for low-efficiency voltage doublers. A prototype has been fabricated and measured. The results show that the proposed RFEH can capture signals at 5.2 GHz and amplify the induced voltage across the antenna. The efficiency exceeds 70% in the frequency range of 5–5.4 GHz, with a peak efficiency of 74.82% at an input power of -10 dBm and a 10-k$\Omega $load.
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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) | 0.002 | 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".