Optimized Design and Analysis of Microstrip Antenna using Artificial Neural Network

Nayak, Chetana; Kumar, Pramod

doi:10.1109/ctceec.2017.8455084

Cited by 2 publications

(2 citation statements)

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“…In one of the recent studies, a metamaterial absorber structure is designed and integrated to a four-element array antenna for 1.27 GHz band by Zhang et al [14]. With these developed techniques, artificial neural networks provide accurate solutions and are used in many areas [16][17][18][19][20]. Srivastava et al used artificial neural network for analyzing and estimating microstrip circular patch antenna parameters in S frequency band regime [16].…”

Section: Of 10mentioning

confidence: 99%

“…In addition, Rajaraman et al developed a method to compute the resonance frequency of metamaterial based patch antennas with the help of a artificial neural network [17]. In the same way, Nayak and Kumar used a artificial neural network approach to design a microstrip patch antenna with possible optimization values [18]. In another study, a novel artificial neural network model was developed to estimate antenna performance parameters in X band antenna applications [19].…”

Section: Of 10mentioning

confidence: 99%

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Mutual Coupling Reduction of Cross-Dipole Antenna for Base Stations by Using a Neural Network Approach

et al. 2020

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In this manuscript, a resonator layer is presented for the purpose of reducing the mutual coupling effect between each antenna element of a cross dipole antenna. In design processes, an artificial neural network approach was used for various resonator designs. In the operating frequency band of 2.2–2.7 GHz, 48 different 6 × 6 resonator layers were created and integrated into the cross dipole antenna to reduce transmission and improve isolation between each antenna elements. Moreover, when training an artificial neural network in the Matlab program, 48 different resonator layers were used with the return losses and transmission values of cross dipole antenna elements. After training process, eight unknown resonator designs were tested and accurate results were obtained. Finally, one of the resonator planes, which was obtained from the artificial neural network, was fabricated and experimentally tested, then an accurate result was obtained. This study provides a good solution, especially for improving isolation in multiport antenna systems, using an artificial neural network approach.

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