Space and Frequency Extrapolation for Deep Learning Design of Coupling Matrix for Microwave Filters

作者

  • Tarek Sallam School of Computer Science and Technology Shandong Xiehe University, Jinan 250109, Shandong, China
  • Qun Wang School of Computer Science and Technology Shandong Xiehe University, Jinan 250109, Shandong, China

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https://doi.org/10.13052/2025.ACES.J.401201

关键词:

Convolutional neural network (CNN), coupling matrix, deep learning, microwave filters, radial basis function neural network (RBFNN)

摘要

In this paper, we propose a deep-learning-based neural network namely, a convolutional neural network (CNN) for predicting frequency response of a microwave filter as a function of its extrapolated coupling parameters. Thus, in this paper, coupling properties of a microwave filter comprise its design space. This space characterizes the filter’s frequency response in complex domain. Moreover, we propose a CNN-based method to extrapolate the response in frequency. Thereby, excessive simulations and long computational time can be avoided as opposed to electromagnetic (EM) solvers. The training of the proposed CNN is based on a circuit model. In order to exhibit the robustness of the new technique, it is applied on 5- and 8-pole filters and compared with a shallow neural network namely, radial basis function neural network (RBFNN). The results reveal that the CNN can achieve extrapolation in both design space and frequency for microwave filters with high accuracy and speed. For a 5-pole filter, the percentage root mean square error (RMSE) between ideal and predicted response of CNN is found to be 0.28% and 0.09% for design space exploration (DSE) and frequency extrapolation (FE), respectively. For an 8-pole filter, the percentage RMSE of CNN is found to be 0.38% and 0.11% for DSE and FE, respectively.

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Tarek Sallam was born in Cairo, Egypt, in 1982. He received the B.S. degree in electronics and telecommunications engineering, the M.S. degree in engineering mathematics from Benha University, Cairo, Egypt, in 2004 and 2011, respectively, and the Ph.D. degree in electronics and communications engineering from Egypt-Japan University of Science and Technology, Alexandria, in 2015. In 2006, he joined the Faculty of Engineering at Shoubra, Benha University. In 2019, he joined Huaiyin Institute of Technology, Huai’an, China. In 2022, he joined Qujing Normal University, Qujing. In 2024, he joined the School of Computer Science and Technology, Shandong Xiehe University, Jinan, where he is currently an Associate Professor. He was a Visiting Researcher with the Electromagnetic Compatibility Laboratory, Osaka University, Osaka, Japan. His research interests include evolutionary optimization, neural networks and deep learning, phased array antennas with array signal processing and adaptive beamforming.

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Qun Wang was born in Jinan, China, in 1986. She holds a master’s degree in Engineering Management from Ocean University of China. She served as a director of Shandong Computer Society, a director of Shandong Software Industry-Education Alliance, and a member of Jinan Vocational Education Society. In 2008, she joined Shandong Shichuang Software Training Institute of Ambow Education Group and served as director of the Training Center. In 2022, she joined the School of Computer Science and Technology, Shandong Xiehe University, Jinan, where she currently holds the position of Deputy Dean of Research. Her research interests include machine vision, neural networks, and deep learning.

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已出版

2025-12-30

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[1]
T. . Sallam 和 Q. . Wang, 《Space and Frequency Extrapolation for Deep Learning Design of Coupling Matrix for Microwave Filters》, ACES Journal, 卷 40, 期 12, 页 1145–1154, 12月 2025.

期

2025: Vol 40 Iss 12

栏目

General Submission