Visible to the public Millimeter Wave Microstrip Antenna Design Based on Swarm Intelligence Algorithm in 5G

TitleMillimeter Wave Microstrip Antenna Design Based on Swarm Intelligence Algorithm in 5G
Publication TypeConference Paper
Year of Publication2017
AuthorsJian, R., Chen, Y., Cheng, Y., Zhao, Y.
Conference Name2017 IEEE Globecom Workshops (GC Wkshps)
Keywords5G communication system, 5G mobile communication, Algorithm design and analysis, antenna patch parameter, composability, Elman Neural Network model, Impedance, impedance matching, Microstrip, microstrip antennas, millimeter wave microstrip antenna design, mm-wave, neural nets, nonlinear regression model, optimization algorithm, particle swarm ant colony optimization, particle swarm optimisation, patch parameters, PSACO, pubcrawl, regression analysis, Resonant frequency, return loss characteristic, swarm intelligence, Swarm intelligence algorithm
Abstract

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.

URLhttps://ieeexplore.ieee.org/document/8269196/
DOI10.1109/GLOCOMW.2017.8269196
Citation Keyjian_millimeter_2017