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

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.