In this paper, we investigate the optimality of deep learning-based optimal sparse arrays in comparison to well known conventional sparse linear arrays. Recently, a deep learning-based approach was proposed for antenna selection purposes as a measure towards reducing high hardware and computational cost in radar systems. Through numerical examples, we demonstrated that the proposed approach yields sparse arrays whose performance and configurations are comparably closer to conventional sparse arrays.

antenna selection; sparse arrays; direction-of-arrival estimation; deep learning