  We introduce a novel physical layer scheme for single user Multiple-Input
Multiple-Output (MIMO) communications based on unsupervised deep learning using
an autoencoder. This method extends prior work on the joint optimization of
physical layer representation and encoding and decoding processes as a single
end-to-end task by expanding transmitter and receivers to the multi-antenna
case. We introduce a widely used domain appropriate wireless channel impairment
model (Rayleigh fading channel), into the autoencoder optimization problem in
order to directly learn a system which optimizes for it. We considered both
spatial diversity and spatial multiplexing techniques in our implementation.
Our deep learning-based approach demonstrates significant potential for
learning schemes which approach and exceed the performance of the methods which
are widely used in existing wireless MIMO systems. We discuss how the proposed
scheme can be easily adapted for open-loop and closed-loop operation in spatial
diversity and multiplexing modes and extended use with only compact binary
channel state information (CSI) as feedback.
