  MIMO technology has enabled spatial multiple access and has provided a higher
system spectral efficiency (SE). However, this technology has some drawbacks,
such as the high number of RF chains that increases complexity in the system.
One of the solutions to this problem can be to employ reconfigurable antennas
(RAs) that can support different radiation patterns during transmission to
provide similar performance with fewer RF chains. In this regard, the system
aims to maximize the SE with respect to optimum beamforming design and RA mode
selection. Due to the non-convexity of this problem, we propose machine
learning-based methods for RA antenna mode selection in both dynamic and static
scenarios. In the static scenario, we present how to solve the RA mode
selection problem, an integer optimization problem in nature, via deep
convolutional neural networks (DCNN). A Multi-Armed-bandit (MAB) consisting of
offline and online training is employed for the dynamic RA state selection. For
the proposed MAB, the computational complexity of the optimization problem is
reduced. Finally, the proposed methods in both dynamic and static scenarios are
compared with exhaustive search and random selection methods.
