  MIMO transmit arrays allow for flexible design of the transmit beampattern.
However, the large number of elements required to achieve certain performance
using uniform linear arrays (ULA) maybe be too costly. This motivated the need
for thinned arrays by appropriately selecting a small number of elements so
that the full array beampattern is preserved. In this paper, we propose
Learn-to-Select (L2S), a novel machine learning model for selecting antennas
from a dense ULA employing a combination of multiple Softmax layers constrained
by an orthogonalization criterion. The proposed approach can be efficiently
scaled for larger problems as it avoids the combinatorial explosion of the
selection problem. It also offers a flexible array design framework as the
selection problem can be easily formulated for any metric.
