Optimization of Monopole Consecutive Square-Shaped Patch Antenna with ANN for Wireless Communicaton Systems

Year 2023, , 492 - 505, 30.09.2023

Sena Esen Bayer Keskin , Cem Güler , Batuhan Uslu

https://doi.org/10.31202/ecjse.1247180

Abstract

In recent years, Artificial Neural Network (ANN) applications in antenna structures have gained significant traction due to their potential to reduce design and calculation times, offering optimization and prediction capabilities. This study introduces a novel monopole patch antenna design featuring a consecutive square-shaped broadband microstrip-line-fed antenna. The proposed antenna exhibits an impressive impedance bandwidth of 68% (1.55 - 2.82 GHz), a remarkable return loss of -47.25 dB, and a directivity gain of 2.77 dBi. Simulation studies were conducted using CST™ Studio Suite electromagnetic simulation software. The ANN model developed based on feed-forward backpropagation demonstrates exceptional agreement with the simulation results, showcasing an accuracy of 99.61805% and performing 2769.231 times faster. With advancing technology, ANNs present an effective solution for addressing complex antenna design challenges arising from escalating data rate requirements and uninterrupted data transmission. These results open promising avenues for further advancements in antenna design aided by ANN applications.

Keywords

Artificial Neural Network, Feed-Forward Backpropagation, Monopole Patch Antenna, Wireless Communication Systems, Bandwidth

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