Deep Learning Based Near-OrthogonalSuperposition Code for Short Message Transmission

Massive machine type communication (mMTC) hasattracted new coding schemes optimized for reliable short mes-sage transmission. In this paper, a novel deep learning basednear-orthogonal superposition (NOS) coding scheme is proposedfor reliable transmission of short messages in the additive whiteGaussian noise (AWGN) channel for mMTC applications. Sim-ilar to recent hyper-dimensional modulation (HDM), the NOSencoder spreads the information bits to multiple near-orthogonalhigh dimensional vectors to be combined (superimposed) into asingle vector for transmission. The NOS decoder first estimatesthe information vectors and then performs a cyclic redundancycheck (CRC)-assistedK-best tree-search algorithm to furtherreduce the packet error rate. The proposed NOS encoder anddecoder are deep neural networks (DNNs) jointly trained asan auto-encoder and decoder pair to learn a new NOS codingscheme with near-orthogonal codewords. Simulation results showthe proposed deep learning-based NOS scheme outperformsHDM and Polar code with CRC-aided list decoding for short(32-bit) message transmission.