Utilizing Transmission Lines for Efficient Energy Harvesting in 5G Networks
Bibliographic record
Abstract
With the growing adoption of 5G technology, the proliferation of low-power sensors for the Internet of Things (IoT) is expected to increase considerably. As the number of IoT sensors grows, maintaining their batteries becomes increasingly challenging. To overcome this challenge, researchers are exploring energy harvesting solutions to self-power IoT sensors. In 5G networks, the high density of antennas ensures that a nearby antenna is available to provide sufficient RF energy for powering sensors. However, extracting energy efficiently from available high-frequency RF sources is a challenging task. The efficiency of conventional RF-to-DC converters drops significantly as the frequency increases and the input power decreases. This work presents a novel approach that maintains high efficiency even at low RF input power levels where conventional methods fail. In the proposed solution, a passive transmission-line network is utilized to boost the voltage induced by an incoming RF signal. This approach not only improves efficiency but also enables energy extraction from weak incoming signals that cannot be harvested otherwise. Simulation results indicate that an efficiency of 78% can be achieved at 5.2 GHz with an input signal power as low as −20 dBm.
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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 | 0.001 | 0.001 |
| 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".