  In the past few years, deep learning (DL) techniques have been introduced for
designing sparse arrays. These methods offer the advantages of feature
engineering and low prediction-stage complexity, which is helpful in tackling
the combinatorial search inherent to finding a sparse array. In this chapter,
we provide a synopsis of several direction finding applications of DL-based
sparse arrays. We begin by examining supervised and transfer learning
techniques that have applications in selecting sparse arrays for a cognitive
radar application. Here, we also discuss the use of meta-heuristic learning
algorithms such as simulated annealing for the case of designing
two-dimensional sparse arrays. Next, we consider DL-based antenna selection for
wireless communications, wherein sparse array problem may also be combined with
channel estimation, beamforming, or localization. Finally, we provide an
example of deep sparse array technique for integrated sensing and
communications (ISAC) application, wherein a trade-off of radar and
communications performance makes ISAC sparse array problem very challenging.
For each setting, we illustrate the performance of model-based optimization and
DL techniques through several numerical experiments. We discuss additional
considerations required to ensure robustness of DL-based algorithms against
various imperfections in array data.
