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Neural Computing and Applications
Published in: Neural Computing and Applications 7/2018

30-10-2017 | Original Article

Power-pattern synthesis for energy beamforming in wireless power transmission

Authors: Geng Sun, Yanheng Liu, Han Li, Jionghui Li, Aimin Wang, Ying Zhang

Published in: Neural Computing and Applications | Issue 7/2018

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Abstract

This paper proposed a power-pattern optimization method for suppressing the maximum side lobe level outside of the collection region (CSL) of energy beamforming for wireless power transmission based on the biogeography-based optimization with local search (BBOLS). Two improved components, local search operator and selection operator, are introduced into the normal biogeography-based optimization to improve the performance of the algorithm. These two introduced factors can significantly help the algorithm to improve the convergence rate, prevent the candidate solutions from being trapped into the local optimum. Simulation results show that the CSL of the planar antenna array obtained by BBOLS can be depressed effectively while the beam collection efficiency can be enhanced. Moreover, the accuracy and the convergence rate of BBOLS are better than other algorithms. In addition, the power-pattern performance obtained by BBOLS is also verified by the electromagnetic simulations.
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Metadata
Title
Power-pattern synthesis for energy beamforming in wireless power transmission
Authors
Geng Sun
Yanheng Liu
Han Li
Jionghui Li
Aimin Wang
Ying Zhang
Publication date
30-10-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3255-6

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