Millimeter Wave Microstrip Antenna Design Based on Swarm Intelligence Algorithm in 5G

In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.

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