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Optimization of ultra-wide band antenna by selection of substrate material using artificial neural network

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Abstract

This paper suggests the implementation of artificial neural networks (ANNs) as a tool to design and optimize antenna for WBAN applications. Substrate selection plays a crucial role in antenna design. The substrates used in this work are RT 5880, Quartz, Rubber, RT 6010, FR-4. Further, a dataset of 198 samples has been trained and validated using the scaled conjugate gradient algorithm. The geometry of antenna is obtained as the function of input variables which are height of dielectric substrate (Hs), dielectric constant of the substrates (εr), and width & height of feed. The impedance of the antenna is used as the output variable. To optimize the design attributes of the antenna, ANN is implemented, and the mean squared error value of 0.11123 is obtained for RT 5880 as substrate having dielectric constant of 2.2 with the substrate height 3.2 mm, the width of feed 1.7 mm, and feed thickness of 0.35 mm, which are used in designing the optimized antenna. The dimensions of the antenna are 34 × 34 × 3.65 mm3. The resonant frequency for the optimized design is obtained at 6.415 & 8.999 GHz, with a gain of 4.051 dBi & 4.457 dBi, respectively, and S11 of −25.8842 dB & −18.734 dB, respectively. S11 is the reflection coefficient of an antenna that quantifies how much power is an antenna reflects. When designing an antenna, the process of selecting the substrate material and design parameters can take a long time. With the help of ANN, as discussed in this research work, the design parameters and substrate material that can provide better performance can be selected in less time, which will benefit the design engineer.

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  1. Department of Electronics and Communications, Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India

    P. Uday Ashish & Sindhu Hak Gupta

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  1. P. Uday Ashish
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Ashish, P.U., Gupta, S.H. Optimization of ultra-wide band antenna by selection of substrate material using artificial neural network. Appl. Phys. A 128, 192 (2022). https://doi.org/10.1007/s00339-022-05312-7

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