Optimization of Metamaterial Unit Cell Using Radial Basis Function Neural Network

Abstract

Microstrip Patch Antennas (MPA) are being used more and more in current communication systems because of their advantages such as being Lightweight, easy to construct, and low cost. However, MPA operational bandwidth and power handling capabilities are restricted. In this research, a novel unit cell MPA is designed and optimized using a Radial Basis Function Neural network (RBFNN). Flame-retardant (FR4) metamaterial is used in the fabrication of the envisioned antenna and the device. The High-Frequency Structure Simulator (HFSS) version 15 software is used for the design and simulation of the model. The design is simulated at a frequency range of 2 to 6 Hertz. Finally, the implementation of the antenna is performed using Complementary Split Ring Resonator (CSRR) technique. The proposed structure produces an excellent reflection coefficient, and Voltage Standing Wave Ratio (VSWR), which are –15.12 at 1.5 GHz, –55.41 at 2.5 GHz, and –25.63 dB at 3.5 GHz and 2.0 dB respectively. Simulation results show an excellent outcome, as return losses are 23.18, 38.67, and 44.12 dB at 0.6, 1.7, and 3.5 GHz respectively, and the gain is 8.5 dB at 6 GHz, which are quite similar to the actual values. The proposed unit cell antenna outperformed the other previously designed microstrip antenna and is suitable for wireless communication systems.