Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation-based neural network model

Abstract

In this work, design optimization of a varicap diode loaded antenna consisting of four identical rectangular microstrips is presented as a pattern reconfigurable antenna at 5.2 GHz. The microstrips are printed on the front of a FR4 substrate with the dimensions of 40 mm x 25 mm and epsilon(r) = 4.6, h = 1.58 mm and probe-fed via a coupling using a rectangular microstrip line symmetrically placed between them. In first stage, S-11 of the antenna are obtained as its real and imaginary parts as continuous functions of geometry of the microstrip components within 3 to 7 GHz using multi-layer perceptron (MLP) trained and validated by 3D EM simulated data. In order to determine the most suitable (MLP) architecture and training algorithm, 20 different MLP architectures are tested. Then, S-11 are optimized with respect to the geometry parameters using differential evolution algorithm and MLP based model. The antenna is prototyped with the optimally selected parameters and measured. From the comparison of simulation and measurement results, it can be observed that the measurement results agree with the simulation results, thus it can be concluded that the proposed antenna is a simple and successful design subject to the design purposes with together its design methodology.