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- W4283781690 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." @default.
- W4283781690 created "2022-07-03" @default.
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- W4283781690 date "2022-06-30" @default.
- W4283781690 modified "2023-09-23" @default.
- W4283781690 title "Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission" @default.
- W4283781690 doi "https://doi.org/10.48550/arxiv.2206.15065" @default.
- W4283781690 hasPublicationYear "2022" @default.
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