  Massive machine type communication (mMTC) has attracted new coding schemes
optimized for reliable short message transmission. In this paper, a novel deep
learning-based near-orthogonal superposition (NOS) coding scheme is proposed to
transmit short messages in multiple-input multiple-output (MIMO) channels for
mMTC applications. In the proposed MIMO-NOS scheme, a neural network-based
encoder is optimized via end-to-end learning with a corresponding neural
network-based detector/decoder in a superposition-based auto-encoder framework
including a MIMO channel. The proposed MIMO-NOS encoder spreads the information
bits to multiple near-orthogonal high dimensional vectors to be combined
(superimposed) into a single vector and reshaped for the space-time
transmission. For the receiver, we propose a novel looped K-best tree-search
algorithm with cyclic redundancy check (CRC) assistance to enhance the error
correcting ability in the block-fading MIMO channel. Simulation results show
the proposed MIMO-NOS scheme outperforms maximum likelihood (ML) MIMO detection
combined with a polar code with CRC-assisted list decoding by 1-2 dB in various
MIMO systems for short (32-64 bit) message transmission.
