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Artificial Intelligence based Antenna Design for Future Millimeter Wave Wireless Communication in Fifth Generation

机译:基于人工智能的第五代未来毫米波无线通信天线设计

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In this paper, we proposed an antenna, which is suitable for the millimeter wave rehash. The single band antenna contains new opening stacked on the oozing patch with the 50 ohms microstrip line enabling utilized. This single band antenna was reproduced on a FR4 dielectric substrate have relative permittivity 4.4, catastrophe redirection 0.02, and stature 1.6 mm. The antenna used to be reenacted by using Electromagnetic propagation, PC programming improvement High Frequency Structural Simulator. The parameters of the results properly clear and exhibited the composition for millimeter wave 5G faraway application at 38 GHz. When all is said in done, antenna necessities are determined for a lot of frequencies or recurrence band. This paper proposes a cross breed technique utilizing artificial knowledge to structure and improve the array antennas. Right off the bat, a direct array antenna is structured in matlab programming. A blunder in tilt is acquainted physically with the array components. Thinking about this as a mutilation progressively, this blunder is enhanced utilizing Artificial Neural Networks calculations. The neural system chips away at the demonstrating and improving the antenna arrays, by working on the geometric parameters and by considering some foreordained criteria. Artificial Intelligence is the instrument used to upgrade the mistake since it is can create the outcomes extremely quick contrasted with some other diverse improvement calculations. The outcomes are recreated utilizing Aerial tool kit and Artificial Neural Network tool compartment in Matlab
机译:在本文中,我们提出了一种适用于毫米波回波的天线。单频带天线包含一个新的开口,该开口堆叠在渗出的贴片上,并启用了50欧姆微带线。将该单频带天线复制到具有相对介电常数4.4,巨灾重定向0.02和身高1.6 mm的FR4介电基板上。该天线以前是通过电磁传播,PC编程改进的高频结构模拟器来重新制定的。结果的参数正确清晰,在38 GHz的毫米波5G遥远应用中显示出其成分。说完所有内容后,就可以确定很多频率或重复频带的天线必要性。本文提出了一种交叉育种技术,利用人工知识来构造和改进阵列天线。立刻,直接在Matlab编程中构造了直接阵列天线。阵列组件在物理上熟悉倾斜的错误。逐渐将此视为残害,使用人工神经网络计算可以增强这种错误。通过研究几何参数并考虑一些预期的标准,神经系统将无法证明和改进天线阵列。人工智能是用于升级错误的工具,因为与其他多种改进计算相比,人工智能可以非常快速地产生结果。通过在Matlab中使用航拍工具套件和人工神经网络工具仓来重新创建结果

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