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Sparse Array Design for Optimum Beamforming Using Deep Learning

IEEE Transactions on Aerospace and Electronic Systems(2024)SCI 2区SCI 1区

Widener Univ | Villanova Univ

Cited 0|Views16
Abstract
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 exhaustive search by enumerations which is computationally prohibitive for large arrays. The employed Deep Neural Network (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 classification problem where the sparse array configuration achieving MaxSINR is one-hot encoded, and indicated by the output layer of DNN. We evaluate the performance of the DNN in terms of the sparse array classification accuracy as well as in terms of the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that the DNN effectively learns the optimal sparse configuration which has desirable SINR characteristics, hence paving the way for efficient real-time implementation.
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Key words
Sensor arrays,Interference,Sensors,Array signal processing,Signal to noise ratio,Training,Signal processing algorithms,Deep neural network (DNN),maximum signal-to-interference plus noise ratio (MaxSINR),sparse arrays
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要点】:利用深度学习实现最大信噪比的稀疏阵列设计方法,通过有效的干扰抑制和信号增强,实现了最佳性能,为实时实现提供了可能性。

方法】:使用监督神经网络进行设计,采用了高效的稀疏波束形成光谱分析方法生成类别标签,并通过DNN有效逼近了输入数据的空间相关性与稀疏配置输出之间的映射关系。

实验】:通过实验评估了DNN在稀疏阵列分类准确性和干扰抑制、信号增强能力上的表现,得出DNN能有效学习最佳稀疏配置并具有良好SINR特性,为实时实现铺平了道路。数据集名称和结果未提及。