Two-Order Deep Learning for Generalized Synthesis of Radiation Patterns for Antenna Arrays

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Abstract

This article tackles the generalized synthesis of antenna arrays using two-order deep learning. Existing deep learning-assisted antenna synthesis approaches mainly rely on model training using electromagnetic (EM) simulation data, and hence feature limited generalization ability and the need for a huge amount of EM simulations. The proposed two-order deep learning method uses the first-order model to learn the generic features of radiation patterns from the data efficiently generated by applying conventional array factors. After that, the second-order model learns from EM simulations to capture the detailed pattern variations due to concrete coupling effects in the case of a specific array arrangement with different operating frequencies, radiation structures, and feeding schemes. Therefore, the two-order DL model can predict the radiation patterns of a series of antenna arrays while reducing the needed amount of EM simulation data. Implementation is carried out on a series of patch antenna arrays to verify the feasibility and robustness of the proposed approach. The validation includes conditions for the array operating at arbitrary new frequencies, with modified radiation structures or new feeding schemes. The results show that the proposed two-order model provides a prediction accuracy of about 84% for a series of 1 × 4 antenna arrays, clearly outperforming the existing regular one-order DL model, which obtains around 65% with the same EM simulation data. The proposed method reveals a promising direction for applying deep learning to assist the design and analysis of antenna arrays.

Original languageEnglish
JournalIEEE Transactions on Artificial Intelligence
Volume4
Issue number5
Pages (from-to)1359-1368
Number of pages10
ISSN2691-4581
DOIs
Publication statusPublished - 1 Oct 2023

Keywords

  • Antenna array
  • Antenna arrays
  • Antenna radiation patterns
  • Arrays
  • Computational modeling
  • Data models
  • Deep learning
  • Training
  • deep learning
  • deep neural network
  • generalized synthesis
  • radiation pattern
  • two-order

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