Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach

This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31...
Ausführliche Beschreibung

This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0  × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications. Ausführliche Beschreibung