Title: Compact microstrip patch antenna design using a deep belief neural network for wireless application

Authors: S. Sandhya Rani; K. Kumar Naik

Addresses: Department of Electronics and Communication Engineering, Jayamukhi Institute of Technological Sciences, Warangal, 506332, Telangana, India ' Department of Electronics and Communication Engineering, Koneru Lakshamaiah Educational Foundation (Deemed to be University), Guntur, India

Abstract: This paper presents a deep belief neural network (DBN) to design an inset-fed E-shaped microstrip patch antenna. To design the shape of such an antenna, a DBN is proposed. This paper is presented to design of a compact patch microstrip antenna with an operating frequency of 0.75-2.24 GHz and 3-3.46 GHz. The upper and lower notches maintain the same dimensions throughout the design process. Notch length and width are set for the investigation purpose. The proposed work utilises the optimal DBNN model for the designing of the antenna in terms of area and therein significantly maximises the bandwidth usage and is also used for simulation purposes. The outcomes are analysed and compared with state-of-art works and show our proposed approach shows the reduced area with the maximised bandwidth usage.

Keywords: microstrip patch antenna; E-shaped microstrip patch antenna; deep DBN; belief neural network; bandwidth and area.

DOI: 10.1504/IJSSE.2025.146194

International Journal of System of Systems Engineering, 2025 Vol.15 No.2, pp.99 - 111

Received: 21 Nov 2022
Accepted: 24 Dec 2022
Published online: 12 May 2025 *

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