Cognitive Learning-Aided Multi-Antenna Communications
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by
Ahmet M. Elbir, Kumar Vijay Mishra
2020
Abstract
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.
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