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 × 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.

中文翻译：

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相控阵天线二维自适应波束形成的卷积神经网络对阵列缺陷具有鲁棒性

由于其高计算复杂性，实现相控阵天线的稳健且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，本文提出了一种基于深度学习的波束形成方法。特别是，通过将问题建模为卷积神经网络 (CNN) 来计算最佳权重向量，该卷积神经网络使用从最佳维纳解获得的 I/O 对进行训练。为了展示新技术的鲁棒性，将其应用于8×8相控阵天线，并与浅（非深）神经网络即径向基函数神经网络进行比较。结果表明，即使在存在阵列缺陷的情况下，CNN 也能产生近乎最优的维纳权重。