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Prospect of Dynamic Metasurface Array Antenna System With Machine Learning

Prospect of Dynamic Metasurface Array Antenna System With Machine Learning

Rupesh Kumar
Copyright: © 2023 |Pages: 18
ISBN13: 9781668482872|ISBN10: 1668482878|ISBN13 Softcover: 9781668482919|EISBN13: 9781668482889
DOI: 10.4018/978-1-6684-8287-2.ch003
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MLA

Kumar, Rupesh. "Prospect of Dynamic Metasurface Array Antenna System With Machine Learning." Metamaterial Technology and Intelligent Metasurfaces for Wireless Communication Systems, edited by Shilpa Mehta and Arij Naser Abougreen, IGI Global, 2023, pp. 64-81. https://doi.org/10.4018/978-1-6684-8287-2.ch003

APA

Kumar, R. (2023). Prospect of Dynamic Metasurface Array Antenna System With Machine Learning. In S. Mehta & A. Abougreen (Eds.), Metamaterial Technology and Intelligent Metasurfaces for Wireless Communication Systems (pp. 64-81). IGI Global. https://doi.org/10.4018/978-1-6684-8287-2.ch003

Chicago

Kumar, Rupesh. "Prospect of Dynamic Metasurface Array Antenna System With Machine Learning." In Metamaterial Technology and Intelligent Metasurfaces for Wireless Communication Systems, edited by Shilpa Mehta and Arij Naser Abougreen, 64-81. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8287-2.ch003

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Abstract

This chapter presents the machine learning (ML) concept for standard RF component design in microwave frequency. It will explain the use of the deep machine learning concept for antenna and other RF components, such as RF filter, and all relevant analysis will be based on the CST simulations. The comparative study of the ML approach and the antenna design tool (such as CST) will be presented in the form of their performance. This chapter will explain the perspectives of ML in RF system design and analysis. This chapter will present the design of the antenna and filter as examples. The simulated results will be obtained by the CST MW Studio and the ML will be implemented in MATLAB.

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