Design and Analysis of a Miniaturized-Metamaterial-Based Monopole Antenna for Ultra-Wide Band Wireless Communication System Using Machine Learning
Résumé
This research presents a miniaturized microstrip Ultra-Wideband (UWB) antenna based on double square ring Metamaterial (MTM) for modern wireless communication devices. The MTM unit cells are placed on the back of the antenna to help improve antenna parameters (i.e., bandwidth, gain, radiation pattern, and efficiency). The antenna is based on an FR-4 substrate with a thickness of 1.5 mm, a relative permittivity of 4.4, and a dielectric loss tangent of 0.02. To achieve a solution in the frequency domain, simulations were performed using Ansys High-Frequency Structure Simulator (HFSS) software, an electromagnetic wave simulator based on a Finite Element Method (FEM)). Furthermore, a feeder with a characteristic impedance of 50 Ω has been applied. To address the computational time challenges associated with electromagnetic simulators like HFSS, Machine Learning (ML) in the form of an Artificial Neural Network (ANN) has been employed to optimize the antenna prior to the implementation of the MTM structure. The antenna's overall surface area is 24 × 12 mm2, with an absolute bandwidth of 15.53 GHz, ranging from 3.83 GHz to 19.36 GHz. The maximum efficiency and gain are 98.79 % and 4.72 dB, respectively. The UWB antenna demonstrates superior performance in comparison to the results reported in recent studies employing MTMs and ML principles for optimization.
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