Cognitive Learning-Aided Multi-Antenna Communications

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. The DL-based solutions tackle these problems at the various stages of communications processing such as channel estimation, hybrid beamforming, user localization, and sparse array design. There are research opportunities to address significant design challenges arising from insufficient data coverage, learning model complexity, and data transmission overheads. This article provides synopses of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications.