An Intelligent Antenna Synthesis Method Based on Machine Learning

TLDR

The study proposes an intelligent antenna synthesis method that automatically selects antenna type and optimizes geometric parameters to meet performance requirements. The method analyzes antenna performance, builds an SVM-based selection model, and employs a stacking ensemble of five learners to predict optimal geometries, demonstrated on microstrip patch, spiral, and horn antennas. The system achieves over 99 % classification accuracy and less than 6 % MAPE in parameter prediction, outperforming traditional data‑driven models and demonstrating practical applicability for intelligent antenna design.

Abstract

An intelligent antenna synthesis method is proposed to automatically select suitable antenna type and provide optimal geometric parameters according to the requirement of antenna performance. The performance characteristics of different antennas are analyzed, and a smart antenna selection model is established to recommend the appropriate antenna by using support vector machine (SVM). Then, the stacking ensemble model is built by combining five primary learners to give full play to the advantages of each type of learners and, finally, the geometric parameters of the antenna are provided. Microstrip patch antenna, spiral antenna, and horn antenna are used to demonstrate the accuracy and efficiency of the model. The system shows a great ability of antenna classification with accuracy over 99% and parameter prediction with a mean absolute percentage error (MAPE) of less than 6%. Moreover, the results are compared with the traditional data-driven model, and the key parameters of the model are comprehensively discussed. The proposed method could be widely used in intelligent antenna design in practice.