A High-Efficiency and Reconfigurable Rectenna Array for Dynamic Output DC Power Control Article Swipe

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Yuchao Wang , Yangkun Song , Bohan Zhang , Shaonan Chen , Qiang Chen , Cheng Zhang ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.3389/fphy.2022.866656

In this paper, an output power reconfigurable rectenna array is proposed for the first time to harvest RF energy. The proposed rectenna array can be used to capture abundant RF energy with high conversion efficiency in a wide range of input power levels and load impedance at 1.88, 2.35, 3.5, and 4.9 GHz simultaneously. The output DC power of the proposed rectenna array can be changed exponentially by altering the on-off state of the switches located between the antenna and the rectifier. The measured result shows that the output power is linearly related to the number of opened switches. Therefore, the proposed rectenna array can be flexibly applied to battery-free or low-power-requiring devices according to their different power needs.

Related Topics

Rectifier (Neural Networks)

Electrical Engineering

Electronic Engineering

Recurrent Neural Network

Aerospace Engineering

Quantum Mechanics

Machine Learning

Concepts

Rectenna Rectifier (neural networks) Power (physics) Electrical engineering Energy conversion efficiency Antenna (radio) Battery (electricity) Range (aeronautics) Electrical impedance Computer science Electronic engineering Engineering Voltage Physics Rectification Stochastic neural network Recurrent neural network Aerospace engineering Artificial neural network Quantum mechanics Machine learning

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Type: article
Language: en
Landing Page: https://doi.org/10.3389/fphy.2022.866656
PDF: https://www.frontiersin.org/articles/10.3389/fphy.2022.866656/pdf
OA Status: gold
Cited By: 5
References: 17
Related Works: 10
OpenAlex ID: https://openalex.org/W4214524275

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DOI: https://doi.org/10.3389/fphy.2022.866656

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Title: A High-Efficiency and Reconfigurable Rectenna Array for Dynamic Output DC Power Control

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-02-28

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Authors: Yuchao Wang, Yangkun Song, Bohan Zhang, Shaonan Chen, Qiang Chen, Cheng Zhang

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Landing page: https://doi.org/10.3389/fphy.2022.866656

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PDF URL: https://www.frontiersin.org/articles/10.3389/fphy.2022.866656/pdf

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Concepts: Rectenna, Rectifier (neural networks), Power (physics), Electrical engineering, Energy conversion efficiency, Antenna (radio), Battery (electricity), Range (aeronautics), Electrical impedance, Computer science, Electronic engineering, Engineering, Voltage, Physics, Rectification, Stochastic neural network, Recurrent neural network, Aerospace engineering, Artificial neural network, Quantum mechanics, Machine learning

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Cited by: 5

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