Automated and Optimized Regression Model for UWB Antenna Design

Antenna design involves continuously optimizing antenna parameters to meet the desired requirements. Since the process is manual, laborious, and time-consuming, a surrogate model based on machine learning provides an effective solution. The conventional approach for selecting antenna parameters is mapped to a regression problem to predict the antenna performance in terms of S parameters. In this regard, a heuristic approach is employed using an optimized random forest model. The design parameters are obtained from an ultrawideband (UWB) antenna simulated using the high-frequency structure simulator (HFSS). The designed antenna is an embedded structure consisting of a circular monopole with a rectangle. The ground plane of the proposed antenna is reduced to realize the wider impedance bandwidth. The lowered ground plane will create a new current channel that affects the uniform current distribution and helps in achieving the wider impedance bandwidth. Initially, data were preprocessed, and feature extraction was performed using additive regression. Further, ten different regression models with optimized parameters are used to determine the best values for antenna design. The proposed method was evaluated by splitting the dataset into train and test data in the ratio of 60:40 and by employing a ten-fold cross-validation scheme. A correlation coefficient of 0.99 was obtained using the optimized random forest model.

• Publication year

  2023

• Venue

  J. Sens. Actuator Networks

• Publication date

  2023-03-10

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.3390/jsan12020023
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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