Multifunctional Antenna Design for Wireless Consumer Electronic Devices: A Soft-Computing Approach

Abstract

A soft-computing-based novel design approach is proposed for the design of multifunctional antennas to be used in wireless consumer electronic devices. With the combined utilization of the particle swarm optimization (PSO) and the merits of the neural network (NN) technique, the developed formulation produces the design of multifrequency fractal antenna structure for specific multiple frequencies and handles the suitable feeding for these multiple frequencies. The role of the developed trained NN is to remove the need for an electromagnetic simulator that is embedded in the optimization loop of the PSO. The involvement of NN in PSO would eliminate the need for a hit-trial approach during parametric optimization for achieving the set goals as per designer’s requirement. However, as the response of the used soft-computing techniques is fast, the developed design formulation takes significantly less time (the order of seconds) in comparison to the design using simulators or other numerical/analytical methods. Different small size Sierpinski gaskets and Koch monopoles are designed and tested using the developed methodology. The effectiveness of the developed approach is cross-checked with simulation and experimental verifications.