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

TL;DR

This paper introduces a deep learning-based near-orthogonal superposition coding scheme for reliable short message transmission in mMTC, outperforming existing methods like HDM and Polar codes in AWGN channels.

Contribution

It proposes a novel deep learning-based NOS coding scheme with joint training of encoder and decoder, enhancing short message reliability in mMTC applications.

Findings

Outperforms HDM and Polar codes in simulations.

Achieves lower packet error rates for 32-bit messages.

Uses deep neural networks for joint encoding and decoding.

Abstract

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.