  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
for reliable transmission of short messages in the additive white Gaussian
noise (AWGN) channel for mMTC applications. Similar to recent hyper-dimensional
modulation (HDM), the NOS encoder spreads the information bits to multiple
near-orthogonal high dimensional vectors to be combined (superimposed) into a
single vector for transmission. The NOS decoder first estimates the information
vectors and then performs a cyclic redundancy check (CRC)-assisted K-best
tree-search algorithm to further reduce the packet error rate. The proposed NOS
encoder and decoder are deep neural networks (DNNs) jointly trained as an auto
encoder and decoder pair to learn a new NOS coding scheme with near-orthogonal
codewords. Simulation results show the proposed deep learning-based NOS scheme
outperforms HDM and Polar code with CRC-aided list decoding for short(32-bit)
message transmission.
