Implementing H-shaped Antenna for Ku-band Applications by Multilayer Perceptron with Reptile Search Algorithm

Abstract In many applications such as aerospace systems, satellites, mobile radar, and other wireless applications, the Microstrip patch antenna (MPA) plays an important role due to its properties like simplicity, lightweight, low cost of production, and compact structure. Low gain, narrow frequency bandwidth, and high return loss are the shortcomings in the existing microstrip patch antenna (MPA) design approaches. Moreover, the developed models of the antenna are hard to design and larger in size. The antenna's geometrical specifications should be optimized to address this problem. In the proposed approach Reptile Search Algorithm (RSA) based Multilayer perceptron (MLP) neural network is employed to design the H-shaped antenna for Ku-band applications. The multilayer perceptron neural network is employed to calculate the fitness value of the RSA. To train the MLP neural network, the dataset is generated using MATLAB software that contains values of the substrate height, dielectric constant, length of the patch, resonant frequency, width of the patch as the input parameters, and the values of gain and return loss as the output parameters. The performance of the optimally designed antenna with the proposed approach is evaluated in terms of the radiation pattern, return loss, Voltage Standing Wave Ratio (VSWR), gain, computation time, directivity, and convergence speed. The experimental and simulation results of the proposed approach show better performance with 8.89 dB gain, -33.06 dB return loss, and 1.07 VSWR.