Transfer Learning for the Behavior Prediction of Microwave Structures

Jiteng Ma, Shuping Dang, Peizheng Li, Gavin T Watkins, Kevin A Morris, Mark A Beach

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
36 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1-4
Number of pages4
JournalIEEE Microwave and Wireless Components Letters
Publication statusPublished - 26 Oct 2022

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