支持深度学习的稀疏阵列Capon波束形成器设计
Sparse Array Capon Beamformer Design Availing Deep Learning
摘要：
本文考虑了稀疏阵列设计接收波束形成实现最大信号干扰噪声比（MaxSINR）。我们开发了一种基于监督神经网络的设计方法，其中使用高效的稀疏波束形成器频谱分析（SBSA）方法生成类标签。SBSA使用未知窄带干扰环境的显式信息来训练网络，并且具有与使用枚举的训练接近的性能，即，穷举搜索，这在计算上对于大阵列是禁止的。所采用的DNN有效地近似从输入接收数据空间相关性到稀疏配置的输出的未知映射，具有有效的干扰抑制能力。该问题被提出为多标签分类问题，其中实现MaxSINR的所选天线位置由DNN的输出层指示。除了评估DNN在分类精度方面的性能外，我们还评估了分类稀疏阵列减轻干扰和最大化信号功率的能力方面的性能。结果表明，即使在分类错误的情况下，其中至少一个传感器位置不匹配的最佳位置，DNN有效地学习次优稀疏配置，具有理想的SINR特性。这表明DNN能够学习所提出的优化算法，从而为高效的实时实现铺平道路。
英文摘要：
The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) approach. SBSA uses explicit information of the unknown narrowband interference environment for training the network and bears close performance to training using enumerations, i.e., exhaustive search which is computationally prohibitive for large arrays. The employed DNN effectively approximates the unknown mapping from the input received data spatial correlations to the output of sparse configuration with effective interference mitigation capability. The problem is posed as a multi-label classification problem where the selected antenna locations achieving MaxSINR are indicated by the output layer of DNN. In addition to evaluating the performance of the DNN in terms of the classification accuracy, we evaluate the performance in terms of the the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that even in the case of miss-classification, where at least one sensor location doesn't match the optimal locations, the DNN effectively learns the sub-optimal sparse configuration which has desirable SINR characteristics. This shows the ability of the DNN to learn the proposed optimization algorithms, hence paving the way for efficient real-time implementation.
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