國立高雄大學圖資館 |

Language: English

Back

Machine Learning-Assisted Design of Metasurface Radomes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning-Assisted Design of Metasurface Radomes.
Author:	Chen, Yi-Huan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	156 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Notes:	Advisor: Chen, Pai-Yen.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Electrical engineering.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30603761
ISBN:	9798379747114

Machine Learning-Assisted Design of Metasurface Radomes.
Chen, Yi-Huan.

Machine Learning-Assisted Design of Metasurface Radomes. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 156 p.

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--University of Illinois at Chicago, 2023.

This item must not be sold to any third party vendors.

Antennas are indispensable to wireless communications. With high frequency band signals applying in 5G channels, the wireless transmission rate is significantly boosted. While 5G technologies are expected to be the foundation for the next generation’s communication systems, the high frequency band leads 5G antennas be very sensitive to the environment. To set up the base stations efficiently, the demand for a fast and accurate measurement method of antenna radiation patterns is urgently needed. In addition, the development of multi-input-multi-output (MIMO) antenna systems also plays an essential role in 5G technologies, which enables high-speed wireless communications, wide diversities, and multiplexing at the same time. However, the mutual coupling effect significantly affects the performance of MIMO antenna systems which not only degrades the data diversity but also reduce the antenna gain. There are many previous works in mutual coupling reduction. However, most of the design only focus on a specific type of antenna systems and are limited to be widely applied. In this thesis, a series of machine learning (ML)-assisted models are represented for an efficient measurement method of antenna radiation pattern and a design method of metasurface radomes that enable mutual coupling reduction induced by the interference of radiation waves. In general, measuring the antenna's radiation pattern is a time-consuming task and is typically limited to specific planes (e.g., E- and H-planes) or angles. The proposed ML-assisted model based on the generative adversarial network (GAN) can restore the antenna radiation pattern from the sparse measurement data, and hence enables a fast and accurate measurement procedure. In addition, an auto-encoder-decoder (AED)-based inverse design model is presented to design an antenna radomes with broadband, wide-angle unidirectional or bidirectional absorption that can reduce mutual coupling in multi-input-multi-output (MIMO) antenna arrays. The proposed ML algorithm can automatically synthesize the metasurface radome to tailor reflection, transmission, and absorption of EM waves in a unidirectional or bidirectional manner. Three representative metasurface design examples, including an ultrathin, unidirectional and bidirectional metasurface-absorber, and a wide-angle unidirectional metasurface-absorber, are presented to validate the proposed ML-assisted design model. The three applications demonstrated that the ML-assisted model has a great potential in solving EM problems.

ISBN: 9798379747114Subjects--Topical Terms:

454503
Electrical engineering.
Subjects--Index Terms:

Radiation pattern

Machine Learning-Assisted Design of Metasurface Radomes.
LDR:03911nmm a2200445 4500 001 655823
005 20240414211940.5
006 m o d
007 cr#unu||||||||
008 240620s2023 ||||||||||||||||| ||eng d
020 $a 9798379747114
035 $a (MiAaPQ)AAI30603761
035 $a (MiAaPQ)0799vireo3569Chen
035 $a AAI30603761
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chen, Yi-Huan. $3 966946
245 1 0 $a Machine Learning-Assisted Design of Metasurface Radomes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 156 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Chen, Pai-Yen.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Antennas are indispensable to wireless communications. With high frequency band signals applying in 5G channels, the wireless transmission rate is significantly boosted. While 5G technologies are expected to be the foundation for the next generation’s communication systems, the high frequency band leads 5G antennas be very sensitive to the environment. To set up the base stations efficiently, the demand for a fast and accurate measurement method of antenna radiation patterns is urgently needed. In addition, the development of multi-input-multi-output (MIMO) antenna systems also plays an essential role in 5G technologies, which enables high-speed wireless communications, wide diversities, and multiplexing at the same time. However, the mutual coupling effect significantly affects the performance of MIMO antenna systems which not only degrades the data diversity but also reduce the antenna gain. There are many previous works in mutual coupling reduction. However, most of the design only focus on a specific type of antenna systems and are limited to be widely applied. In this thesis, a series of machine learning (ML)-assisted models are represented for an efficient measurement method of antenna radiation pattern and a design method of metasurface radomes that enable mutual coupling reduction induced by the interference of radiation waves. In general, measuring the antenna's radiation pattern is a time-consuming task and is typically limited to specific planes (e.g., E- and H-planes) or angles. The proposed ML-assisted model based on the generative adversarial network (GAN) can restore the antenna radiation pattern from the sparse measurement data, and hence enables a fast and accurate measurement procedure. In addition, an auto-encoder-decoder (AED)-based inverse design model is presented to design an antenna radomes with broadband, wide-angle unidirectional or bidirectional absorption that can reduce mutual coupling in multi-input-multi-output (MIMO) antenna arrays. The proposed ML algorithm can automatically synthesize the metasurface radome to tailor reflection, transmission, and absorption of EM waves in a unidirectional or bidirectional manner. Three representative metasurface design examples, including an ultrathin, unidirectional and bidirectional metasurface-absorber, and a wide-angle unidirectional metasurface-absorber, are presented to validate the proposed ML-assisted design model. The three applications demonstrated that the ML-assisted model has a great potential in solving EM problems.
590 $a School code: 0799.
650 4 $a Electrical engineering. $3 454503
650 4 $a Computer engineering. $3 212944
650 4 $a Remote sensing. $3 195948
653 $a Radiation pattern
653 $a MIMO antenna
653 $a Mutual coupling reduction
653 $a ML-assisted model
653 $a Inverse design model
653 $a Generative adversarial network
653 $a Auto-encoder-decoder
690 $a 0544
690 $a 0464
690 $a 0800
690 $a 0799
710 2 $a University of Illinois at Chicago. $b Electrical and Computer Engineering. $3 795466
773 0 $t Dissertations Abstracts International $g 85-01B.
790 $a 0799
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30603761