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

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 to transmit short messages in multiple-input multiple-output (MIMO) c...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 71; no. 9; p. 1
Main Authors: Bian, Chenghong, Hsu, Chin-Wei, Lee, Changwoo, Kim, Hun-Seok
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coders
Codes
Coding
Decoding
Deep learning
Encoding
Error correction
Floating point arithmetic
Iterative decoding
learned coding
learned modulation
Machine learning
Massive machine type communications
Maximum likelihood decoding
Messages
MIMO
MIMO communication
Modulation
near-orthogonal modulation
Neural networks
Receivers
Redundancy
Search algorithms
superposition coding
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary: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 to transmit short messages in multiple-input multiple-output (MIMO) channels for mMTC applications. In the proposed MIMO-NOS scheme, a neural network-based encoder is optimized via end-to-end learning with a corresponding neural network-based detector/decoder in a superposition-based auto-encoder framework including a MIMO channel. The proposed MIMO-NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector and reshaped for the space-time transmission. For the receiver, we propose a novel looped K -best tree-search algorithm with cyclic redundancy check (CRC) assistance to enhance the error correcting ability in the block-fading MIMO channel. For a comprehensive understanding of the proposed MIMO-NOS scheme, we further quantify the gain from individual components/modules in the framework, and analyze the decoding complexity measured by the floating point operations (FLOPs). Simulation results show the proposed MIMO-NOS scheme outperforms maximum likelihood (ML) MIMO detection combined with a polar code with CRC-assisted list decoding by 1 - 2 dB in various MIMO systems for short (32 - 64 bit) message transmission.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2023.3274158