Machine Learning Methods-based Modeling and Optimization of 3-D-Printed Dielectrics around Monopole Antenna
dc.contributor.author | Sharma, Yashika | |
dc.contributor.author | Chen, Xi | |
dc.contributor.author | Wu, Junqiang | |
dc.contributor.author | Zhou, Qiang | |
dc.contributor.author | Zhang, Hao Helen | |
dc.contributor.author | Xin, Hao | |
dc.date.accessioned | 2022-03-24T22:11:37Z | |
dc.date.available | 2022-03-24T22:11:37Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Sharma, Y., Chen, X., Wu, J., Zhou, Q., Zhang, H. H., & Xin, H. (2022). Machine Learning Methods-based Modeling and Optimization of 3-D-Printed Dielectrics around Monopole Antenna. IEEE Transactions on Antennas and Propagation. | en_US |
dc.identifier.issn | 0018-926X | |
dc.identifier.doi | 10.1109/tap.2022.3153688 | |
dc.identifier.uri | http://hdl.handle.net/10150/663783 | |
dc.description.abstract | In this paper, we propose using new Machine Learning (ML)-based optimization methods, as an alternative to traditional optimization methods, for complex antenna designs. This is an efficient methodology to tackle computational challenges, as it is capable of handling a large number of design parameters and is more efficient as well as informative. The proposed technique is applied for modeling gain performance in the principal plane of a monopole antenna when its radiation properties are modified by placing spatially dependent dielectric material around it. Using the proposed methodology, the dielectric constant values are mapped to the gain pattern of the design. We use two ML techniques for this purpose, namely, Gaussian Process (GP) regression and Artificial Neural Network (ANN). Once each of these models is obtained, they are further used for estimating the dielectric constant values that can suggest optimal directions to modify gain patterns for single-beam and multiple-beam patterns rather than the conventional omnidirectional pattern of a monopole antenna. The performance of this technique is compared with heuristic optimization techniques such as genetic algorithms. The proposed method proves to be quite accurate in spite of being a high-dimensional non-linear problem. A prototype of a monopole design with three-beam gain pattern is fabricated and tested. The measurement results agree well with the simulation results. The proposed methodology can provide useful and scalable optimization tools for computationally intensive antenna design problems. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2021 IEEE. | en_US |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en_US |
dc.subject | 3D printing | en_US |
dc.subject | Antenna radiation patterns | en_US |
dc.subject | Antennas | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Computational modeling | en_US |
dc.subject | Dielectric constant | en_US |
dc.subject | Gaussian Process | en_US |
dc.subject | Neurons | en_US |
dc.subject | Optimization | en_US |
dc.title | Machine Learning Methods-based Modeling and Optimization of 3-D-Printed Dielectrics around Monopole Antenna | en_US |
dc.type | Article | en_US |
dc.identifier.eissn | 1558-2221 | |
dc.contributor.department | Department of Electrical and Computer Engineering, University of Arizona | en_US |
dc.contributor.department | Department of Systems and Industrial Engineering, University of Arizona | en_US |
dc.contributor.department | Department of Physics, University of Arizona | en_US |
dc.identifier.journal | IEEE Transactions on Antennas and Propagation | en_US |
dc.description.note | Immediate access | en_US |
dc.description.collectioninformation | This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu. | en_US |
dc.eprint.version | Final accepted manuscript | en_US |
dc.source.journaltitle | IEEE Transactions on Antennas and Propagation | |
dc.source.beginpage | 1 | |
dc.source.endpage | 1 | |
refterms.dateFOA | 2022-03-24T22:11:38Z |