An Energy Harvesting Solution for IoT Devices in 5G Networks
Bibliographic record
Abstract
Internet of Things (IoT) and IoT applications will experience significant growth as the fifth-generation (5G) of wireless technology matures and becomes more widely adopted. How to power on billions of low power wireless IoT devices has initiated a new interest in energy harvesting. The 5G technology will open new opportunities to design circuits to efficiently extract energy from millimeter waves to power on IoT devices. In this paper, an energy harvesting circuit is designed and simulated using Advanced Design System (ADS) which is compatible with 5G technology. A microstrip patch antenna with -17.35 dB return loss at 11.02 GHz is designed and implemented. A rectifier using a Schottky diode is also designed to operate at high frequencies. Simulation results indicate that the proposed solution can extract energy from incoming waves at 11.02 GHz and generate 1.18 V across an IoT sensor with an efficiency of 87%.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".