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Massive machine type communication (mMTC) has attracted new coding schemesoptimized for reliable short message transmission. In this paper, a novel deeplearning-based near-orthogonal superposition (NOS) coding scheme is proposed totransmit short messages in multiple-input multiple-output (MIMO) channels formMTC applications. In the proposed MIMO-NOS scheme, a neural network-basedencoder is optimized via end-to-end learning with a corresponding neuralnetwork-based detector/decoder in a superposition-based auto-encoder frameworkincluding a MIMO channel. The proposed MIMO-NOS encoder spreads the informationbits to multiple near-orthogonal high dimensional vectors to be combined(superimposed) into a single vector and reshaped for the space-timetransmission. For the receiver, we propose a novel looped K-best tree-searchalgorithm with cyclic redundancy check (CRC) assistance to enhance the errorcorrecting ability in the block-fading MIMO channel. Simulation results showthe proposed MIMO-NOS scheme outperforms maximum likelihood (ML) MIMO detectioncombined with a polar code with CRC-assisted list decoding by 1-2 dB in variousMIMO systems for short (32-64 bit) message transmission.

Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission