  Cognitive communications have emerged as a promising solution to enhance,
adapt, and invent new tools and capabilities that transcend conventional
wireless networks. Deep learning (DL) is critical in enabling essential
features of cognitive systems because of its fast prediction performance,
adaptive behavior, and model-free structure. These features are especially
significant for multi-antenna wireless communications systems, which generate
and handle massive data. Multiple antennas may provide multiplexing, diversity,
or antenna gains that, respectively, improve the capacity, bit error rate, or
the signal-to-interference-plus-noise ratio. In practice, multi-antenna
cognitive communications encounter challenges in terms of data complexity and
diversity, hardware complexity, and wireless channel dynamics. DL solutions
such as federated learning, transfer learning and online learning, tackle these
problems at various stages of communications processing, including
multi-channel estimation, hybrid beamforming, user localization, and sparse
array design. This article provides a synopsis of various DL-based methods to
impart cognitive behavior to multi-antenna wireless communications for improved
robustness and adaptation to the environmental changes while providing
satisfactory spectral efficiency and computation times. We discuss DL design
challenges from the perspective of data, learning, and transceiver
architectures. In particular, we suggest quantized learning models, data/model
parallelization, and distributed learning methods to address the aforementioned
challenges.
