Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
Source abstract
Abstract
In this paper, we propose a method for near-field-based 5G sub 6-GHz array antenna diagnosis using transfer learning. A classification network was implemented for normal/abnormal operation of the array antenna and the failure of a specific port. Furthermore, a regression network that could predict the amplitude and phase of the excitation signal of the array antenna was employed. Additionally, to accelerate the array antenna diagnosis, several near-field lines were sampled and reflected in the regression network. The proposed method was verified by measuring a fabricated 5G sub-6 GHz band 4×4 array antenna in various scenarios using a divider and coaxial cables. The tests showed that the trained network accurately diagnosed 29 of 30 measurement results.
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Research context
OpenAlex topics: Integrated Circuits and Semiconductor Failure Analysis · Electromagnetic Compatibility and Measurements · Microwave Engineering and Waveguides
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Affiliations
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 440-746, Korea
Global Technology Center, Samsung Electronics Co., Ltd., Suwon 443-742, Korea
References (17)
Show source-provided references
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