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首页> 外文期刊>International Journal of Microwave and Wireless Technologies >Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections
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Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

机译：用于阵列缺陷的稳健性的分阶段阵列天线的2D自适应波束成形的卷积神经网络

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摘要

Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 x 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.
机译：由于其高计算复杂性，实现了相控阵天线的稳健和快速的二维自适应波束形成是一个具有挑战性的问题。 为了解决这个问题，本文介绍了一种基于深度学习的波束形成方法。 特别地，通过将问题建模为卷积神经网络（CNN）来计算最佳权重向量，其用从最佳维纳解决方案获得的I / O对训练。 为了表现出新技术的稳健性，它应用于8×8相位的阵列天线，并与浅（非深度）神经网络相比，即径向基函数神经网络。 结果表明，即使在存在阵列缺陷的情况下，CNN也会导致几乎最佳的维纳权重。

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