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Transfer Learning for the Behavior Prediction of Microwave Structures
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  • Jiteng Ma ,
  • Shuping Dang ,
  • Peizheng Li ,
  • Gavin Watkins ,
  • Kevin Morris ,
  • Mark Beach
Jiteng Ma
University of Bristol

Corresponding Author:[email protected]

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Shuping Dang
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Peizheng Li
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Gavin Watkins
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Kevin Morris
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Mark Beach
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Microwave structures behavior prediction is an important research topic in radio frequency (RF) design. In recent years, deep-learning-based techniques have been widely implemented to study microwaves, and they are envisaged to revolutionize this arduous and time-consuming work. However, empirical data collection and neural network training are two significant challenges of applying deep learning techniques to practical RF modeling and design problems. To this end, this letter investigates a transfer-learning-based approach to improve the accuracy and efficiency of predicting microwave structure behaviors. Through experimental comparisons, we validate that the proposed approach can reduce the amount of data required for training while shortening the neural network training time for the behavior prediction of microwave structures.
Feb 2023Published in IEEE Microwave and Wireless Technology Letters volume 33 issue 2 on pages 126-129. 10.1109/LMWC.2022.3214467