Machine Learning Enabled Al 2O 3 Ceramic Based Dual Band Frequency Reconfigurable Dielectric Antenna for Wireless Application
IEEE Transactions on Dielectrics and Electrical Insulation（2024）
摘要
A ceramic (
Al
2
O
3
) material based dual-band high-tuning range frequency reconfigurable dielectric antenna for wireless applications with Machine Learning (ML) algorithm is presented in this article. The proposed antenna is a hybrid structure in which the antenna radiator is designed with a Dielectric Resonator (DR) (Alumina (
Al2
O
3
) ceramic material with a relative dielectric constant (∈
r
)=9.8. The presented work offers dual-band, compactness, and frequency reconfigurability (FR).FR is obtained through two PIN diode switches, operating in ON-ON, ON-OFF, OFF-ON and OFF-OFF configurations. It offers a total spectrum and a maximum wide tuning range of 71.49 % and 44.44 %, respectively. Dual-band is generated through the excitation of
HEM
11δ
, and
HEM
12δ
mode in cylindrical Dielectric Resonator (CDR). In contrast, compactness is obtained through the higher-order mode excitation and hybrid structure. The proposed antenna is designed on the ANSYS HFSS software and optimized through various ML algorithms such as K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Decision Tree (DT), Extreme Gradient Boosting (XGB), and Random Forest (RF). In all configurations, KNN achieved more than 99 % accuracy for the prediction of reflection coefficient (
s
11
). The proposed antenna is used for WiMAX, WLAN, and 5G wireless applications.
更多查看译文
关键词
Alumina (Al2o3),Dielectric Resonator,Machine Learning,Dual-band,Frequency Reconfigurable,5G
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要