Abstract
To make communication viable and efficient, microstrip patch antenna is used. These antennas are low profile, lightweight and are smaller in size. A dataset is created where the resonant frequency, i.e., terahertz band (0.3–3 THz), dielectric constant and height, is assumed beforehand, and length and width of the patch are calculated using the general formula of finding antenna dimensions. Backpropagation is one of the algorithms that is used to train datasets for ANN. This algorithm is fast in convergence, its predictions are accurate, and it takes less time to train. The results obtained after using the general formula are compared with the optimized dimensions given by the ANN, and it displays a great level of agreement. The antenna is designed with the most optimized dimension from the dataset and analyzed to obtain improved antenna efficiency, antenna gain characteristics and reduced SAR value.
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Kumar, A.K., Kirti, M., Pani, S., Saxena, S. (2022). Optimization of Antenna Parameters Using Neural Network Technique for Terahertz Band Applications. In: Dhawan, A., Tripathi, V.S., Arya, K.V., Naik, K. (eds) Recent Trends in Electronics and Communication. VCAS 2020. Lecture Notes in Electrical Engineering, vol 777. Springer, Singapore. https://doi.org/10.1007/978-981-16-2761-3_4
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DOI: https://doi.org/10.1007/978-981-16-2761-3_4
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