  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.
