毫米波基站天线波束方向图反设计

Jeon Hong Park, C. M. Leite, K. Hwang
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引用次数: 0

摘要

波束形成波束方向图设计是5G NR毫米波频段基站运行的基本要求。波束方向图的设计需要调整天线的移相器值,并且需要昂贵的计算成本才能找到所需的波束方向图。本文提出了一种通过基于深度神经网络(DNN)的模型获得所需波束方向图对应的移相器值的方法,以减少迭代计算成本。通过仿真工具基于提取的数据对DNN模型进行训练,并将训练后的DNN模型使用估计移相值模拟的波束方向图与期望的波束方向图进行比较验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inverse design of antenna beam pattern for millimeter-wave base station applications
Beam pattern design for beamforming is an essential requirement for base station operation in the 5G NR millimeter wave band. A beam pattern design requires tuning the phase shifter value of the antenna, and it takes expensive computational cost to find the desired beam pattern. In this paper, we propose a method to obtain the phase shifter value corresponding to the desired beam pattern through a DNN (Deep neural network) based model to reduce the iterative computational cost. The DNN model is trained based on the extracted data through the simulation tool, and the validation is performed by comparing a simulated beam pattern using estimated phase shifter values through trained DNN model with desired beam pattern.
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