Antenna Design and Optimization Using Machine Learning

CJ_Reddy
CJ_Reddy
Altair Employee
edited November 2020 in Altair HyperWorks

Now-a-days antennas have become an integral and important part of almost any wireless communication system. Altair Feko is used to design a very wide range of antenna types, including wire antennas, microstrip, horn, aperture, lenses, reflector, and conformal antennas as shown below.

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Traditional antenna optimization solves the modified version of the original antenna design for each iteration. Thus, the total time required to optimize a given antenna design is highly dependent on the convergence criteria of the selected algorithm and the time taken for each iteration. The use of machine learning (ML) enables the antenna designer to generate trained mathematical model that replicates the original antenna design and then apply optimization on the trained model. Use of trained model allows to run thousands of optimization iterations in a span of a few seconds.

Fast and intelligent antenna design optimization is possible with the use of Design of Experiments (DOE) and ML. By using automatic processes combining state-of-the-art mathematical methods, predictive modeling and data mining, Altair HyperStudy explores the design space of any system model smartly and efficiently. Users are guided to understand data trends, perform trade-off studies, and optimize design performance and reliability, while considering Multiphysics constraints.

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Using HyperStudy in conjunction with Feko allows application of ML for antenna design optimization. The complete workflow of the machine learning approach for antenna design optimization is detailed in the below steps:

  • Generate training and test data with an appropriate DOE study and numerical simulation.
  • Build a machine learning model based on the generated training data.
  • Validate the machine learning model using the generated test data.
  • If the validation is not successful, generate additional training data or use a more appropriate machine learning approach.

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You can review the video below to learn more about application of ML for antenna design optimization and demos of the process above using various antenna examples.

Antenna Design and Optimization Using Machine Learning - Altair University