论文标题
MIMO短消息传输基于学习的近乎正交的叠加代码
Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission
论文作者
论文摘要
大型机器类型通信（MMTC）吸引了针对可靠的短消息传输优化的新编码方案。在本文中，提出了一种新型的基于深度学习的近核叠加（NOS）编码方案，以在多输入多输出（MIMO）通道中传输短消息，以用于MMTC应用程序。在拟议的MIMO-NOS方案中，基于神经网络的编码器通过端到端学习优化，在基于叠加的自动编码器框架（包括MIMO通道）中，具有相应的基于神经网络的检测器/解码器。提出的MIMO-NOS编码器将信息位扩散到多个接近正交的高维矢量中，将其组合为单个矢量并重塑以进行时空传输。对于接收器，我们提出了一种新颖的循环K-最佳树搜索算法，并具有环状冗余检查（CRC）辅助，以增强块衰减的MIMO通道中的误差纠正能力。仿真结果表明，所提出的MIMO-NOS方案优于最大似然（ML）MIMO检测，结合了极性代码，CRC辅助列表在各种MIMO系统中用1-2 dB解码，用于短（32-64位）消息传输。
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.
