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arXiv · 2206.15065

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

Chenghong Bian·Chin-Wei Hsu·Changwoo Lee·Hun-Seok Kim
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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 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. 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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Chenghong Bian, Chin-Wei Hsu, Changwoo Lee, Hun-Seok Kim. 2022-06-30. Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission. https://arxiv.org/abs/2206.15065

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