Improvement and Optimization of Homogeneous Composite in Array Antennas using Convolutional Neural Network

Subject Areas : Telecommunication field and wave

Gohar Varamini ¹ , Behnam Dorostkar Yaghouti ²

1 - Department of Electrical Engineering- Beyza Branch, Islamic Azad University, Beyza, Iran
2 - Department of Information and Communication Technology- Amin University, Tehran, Iran

Received: 2023-06-09 Accepted : 2023-10-14 Published : 2025-09-23

Keywords: Array antenna, convolutional neural network, deep learning, homogeneous composite, micro-strip patch.,

Abstract :

Antenna structure and performance, bandwidth, gain and guidance are the most important performance indicators. For this purpose, RL homogeneous transmission line is very important due to low loss, phase changes, frequency bandwidth, zero and negative order resonance, miniaturization and easy construction, and is very suitable in the design of broadband and array antennas. The right-left hand structure in the antennas due to the difference in the phase of the right part in the repetition of arrays and the thickness of the layer has phase delay and finally deviation of the radiation pattern. On the other hand, the blockage of the transmission line on the left causes bandwidth restriction and increasing the number of casualties in the system. In this paper, with the help of deep learning (DL), composite defects are solved and optimized arrayed antenna. The proposed antenna transmission line design in the range of 2-7 GHz, optimum resonance frequency of 4.5 GHz and convolution, dual resonance and spiral inductor neural algorithm are loaded onto the patch in four arrays. The use of convolutional neural network (CNN) in the left transmission line compensates for the right phase delay and finally enables optimal phase changes, correction of radiation pattern and continuous scanning of phase arrays. Also, by creating gaps in the microstrip patch, bandwidth limit is removed and the system losses are reduced. Secondary dimensions compared to the primary dimension are reduced to about 60% in size and miniature according to the smart modified model. The results of this improved composite showed an increase in bandwidth of 20.3 and the efficiency of the radiation pattern by more than 96%. On the other hand, small dimensions, appropriate frequency bandwidth and simple network design have been provided.

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