Antenna arrays have been widely used for space and space–time processing to estimate the desired signals in the presence of narrowband and wideband interferences. Estimating the optimal weight vector is a challenging problem in space and space–time processing due to its high computational complexity. The problem escalates when the number of antennas and taps of filters for time-domain processing increases. In this study, a convolutional neural network is employed to estimate nearly-optimal weight vectors even when the number of available snapshots of the received signal is as low as 400 and a signal-to-noise ratio as low as −5 dB. Unlike the conventional processors, the authors proposed method requires no prior knowledge about the direction of arrival of the desired signal. In addition, due to its parallel architecture, the computational complexity of the neural processors is reasonable using a single graphical processing unit on a PC to run the algorithms. Through simulations of the uniform linear array in the presence of narrowband and wideband interferences, they demonstrate that the output signal-to-interference plus noise ratio (SINR) is very close (by <0.5 dB) to the max SINR obtained by the optimal weight vector of antenna arrays.

中文翻译：

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基于深度学习的波束成形可消除干扰

天线阵列已广泛用于时空处理，以在存在窄带和宽带干扰的情况下估计所需信号。由于其计算量大，估计最佳权向量在空间和时空处理中是一个具有挑战性的问题。当天线和用于时域处理的滤波器抽头的数量增加时，该问题加剧。在这项研究中，即使当接收信号的可用快照数量低至400且信噪比低至-5 dB时，仍采用卷积神经网络来估计接近最佳的权向量。与常规处理器不同，作者提出的方法不需要有关所需信号到达方向的先验知识。此外，由于采用并行架构，使用PC上的单个图形处理单元来运行算法，神经处理器的计算复杂度是合理的。通过在存在窄带和宽带干扰的情况下对均匀线性阵列进行仿真，他们证明了输出信噪比与噪声比（SINR）与最佳权重所获得的最大SINR非常接近（<0.5 dB）天线阵列的向量。