TY - GEN
T1 - Optimization of Defected Ground Structure (DGS) Using Genetic Algorithm for Gain Enhancement of Microstrip Antenna
AU - Yusuf, A'isya Nur Aulia
AU - Purnamasari, Prima Dewi
AU - Zulkifli, Fitri Yuli
N1 - Funding Information:
This work is funded by the Indonesian Government Scholarship PMDSU from the Ministry of Research, Technology, and Higher Education (Kemristekdikti) under contract number NKB-353/UN2.RST/HKP.05.00/2021.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Defected ground structure (DGS) is a technique for increasing antenna gain by changing the shape of the ground, without the need to increase the dimensions of the antenna. However, the application of the DGS technique is generally carried out using an inductive approach which requires high computational resources and time consuming for the design process. Therefore, to speed up the DGS design process, machine learning methods, especially genetic algorithms, is used. This study proposes a patch antenna DGS optimization model to increase the patch antenna gain using genetic algorithm so that the DGS design time can be shortened and the design process efficiency can be increased. Based on the simulation results, the DGS design without genetic algorithm is able to increase the bandwidth and gain of the patch antenna by 8.91% and 3.92%, respectively. Meanwhile, the DGS design optimized by genetic algorithm is able to increase the bandwidth and gain of the patch antenna by 84% and 50.86%, respectively. In addition, shorter optimization time is achieved by using genetic algorithm.
AB - Defected ground structure (DGS) is a technique for increasing antenna gain by changing the shape of the ground, without the need to increase the dimensions of the antenna. However, the application of the DGS technique is generally carried out using an inductive approach which requires high computational resources and time consuming for the design process. Therefore, to speed up the DGS design process, machine learning methods, especially genetic algorithms, is used. This study proposes a patch antenna DGS optimization model to increase the patch antenna gain using genetic algorithm so that the DGS design time can be shortened and the design process efficiency can be increased. Based on the simulation results, the DGS design without genetic algorithm is able to increase the bandwidth and gain of the patch antenna by 8.91% and 3.92%, respectively. Meanwhile, the DGS design optimized by genetic algorithm is able to increase the bandwidth and gain of the patch antenna by 84% and 50.86%, respectively. In addition, shorter optimization time is achieved by using genetic algorithm.
KW - DGS
KW - gain enhancement
KW - genetic algorithm
KW - microstrip antenna
UR - http://www.scopus.com/inward/record.url?scp=85130043841&partnerID=8YFLogxK
U2 - 10.1109/APACE53143.2021.9760610
DO - 10.1109/APACE53143.2021.9760610
M3 - Conference contribution
AN - SCOPUS:85130043841
T3 - 2021 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2021
BT - 2021 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2021
Y2 - 20 December 2021 through 22 December 2021
ER -