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

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