Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission
Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission
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DOI：
10.1109/tcomm.2023.3274158
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发表时间：
2022-06
影响因子：
8.3
通讯作者：
Chenghong Bian;Chin-Wei Hsu;Changwoo Lee;Hun-Seok Kim
中科院分区：
文献类型：
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作者：
Chenghong Bian;Chin-Wei Hsu;Changwoo Lee;Hun-Seok Kim
大规模机器类型通信（mMTC）已经吸引了针对可靠的短消息传输而优化的新编码方案。本文提出了一种新的基于深度学习的近正交叠加（NOS）编码方案，用于在多输入多输出（MIMO）信道中传输mMTC应用中的短消息。在所提出的MIMO-NOS方案中，基于神经网络的编码器通过端到端学习与包括MIMO信道的基于叠加的自动编码器框架中的相应的基于神经网络的检测器/解码器进行优化。所提出的MIMO-NOS编码器将信息比特扩展为多个近正交的高维向量，以组合（叠加）成单个向量并进行整形以用于空时传输。对于接收端，我们提出了一种新的循环$K$ -最佳树搜索算法与循环冗余校验（CRC）的援助，以提高在块衰落MIMO信道的纠错能力。为了全面理解所提出的MIMO-NOS方案，我们进一步量化了框架中各个组件/模块的增益，并分析了由浮点运算（FLOPs）测量的解码复杂度。仿真结果表明，在短消息（32 - 64 bit）传输的各种MIMO系统中，所提出的MIMO-NOS方案比结合极化码和CRC辅助列表解码的最大似然（ML）MIMO检测性能高1 - 2 dB。
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.