State-of-the-art: AI-assisted surrogate modeling and optimization for microwave filters

Yu, Y., Zhang, Z., Cheng, Q. S., Liu, B. , Wang, Y., Guo, C. and Ye, T. T. (2022) State-of-the-art: AI-assisted surrogate modeling and optimization for microwave filters. IEEE Transactions on Microwave Theory and Techniques, 70(11), pp. 4635-4651. (doi: 10.1109/TMTT.2022.3208898)

Abstract

Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62071211 and in part by U.K. Engineering and Physical Science Research Council under Grant EP/S013113/1 and Grant EP/M013529/1. The work of Qingsha S. Cheng was supported in part by the National Natural Science Foundation of China (NSFC) and in part by the Ministry of Science and Technology (MOST) of China
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Ms Yi and Liu, Professor Bo
• Authors: Yu, Y., Zhang, Z., Cheng, Q. S., Liu, B., Wang, Y., Guo, C., and Ye, T. T.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Microwave Theory and Techniques
• Publisher: IEEE
• ISSN: 0018-9480
• ISSN (Online): 1557-9670
• Published Online: 06 October 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Microwave Theory and Techniques 70(11): 4635-4651
• Publisher Policy: Reproduced in accordance with the publisher copyright policy