Monopole Antenna Modeling and Optimization for 5G Communication System using Machine Learning

This paper optimizes the design of a monopole antenna using machine learning (ML) techniques such as artificial neural network (ANN) and Gaussian process (GP). The gain performance of a monopole antenna is modeled in the principle plane. The proposed antenna’s gain pattern is then mapped with dielectric constant values in order to achieve seamless connectivity of multiple input multiple output (MIMO) transmission in a 5G network. We developed mathematical estimates for the proposed algorithm and compared the results to current techniques. The simulation results show that the proposed model can support high-capacity 5G communication system services and is accurate when dealing with nonlinear problems. Finally, the simulation model outputs are efficiently validated using measurement results.