Deep Learning Based Near-Orthogonal Superposition Code for Short Message Transmission
Deep Learning Based Near-Orthogonal Superposition Code for Short Message Transmission
复制标题
基于深度学习的短消息传输近正交叠加码
DOI：
10.1109/icc45855.2022.9838685
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Hun
中科院分区：
文献类型：
--
作者：
Chenghong Bian;Mingyu Yang;Chin;Hun
大规模机器类型通信（mMTC）吸引了针对可靠的短消息传输而优化的新编码方案。本文提出了一种基于深度学习的新型近正交叠加（NOS）编码方案，用于在 mMTC 应用的加性高斯白噪声（AWGN）信道中可靠传输短消息。与最近的超维调制 (HDM) 类似，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）为单个向量进行传输。 NOS解码器首先估计信息向量，然后执行循环冗余校验（CRC）辅助的K-best树搜索算法以进一步降低数据包错误率。所提出的 NOS 编码器和解码器是深度神经网络（DNN），联合训练为自动编码器和解码器对，以学习具有近正交码字的新 NOS 编码方案。仿真结果表明，所提出的基于深度学习的 NOS 方案在短（32 位）消息传输方面优于 HDM 和带有 CRC 辅助列表解码的 Polar 码。
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 for reliable transmission of short messages in the additive white Gaussian noise (AWGN) channel for mMTC applications. Similar to recent hyper-dimensional modulation (HDM), the NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector for transmission. The NOS decoder first estimates the information vectors and then performs a cyclic redundancy check (CRC)-assisted K-best tree-search algorithm to further reduce the packet error rate. The proposed NOS encoder and decoder are deep neural networks (DNNs) jointly trained as an auto-encoder and decoder pair to learn a new NOS coding scheme with near-orthogonal codewords. Simulation results show the proposed deep learning-based NOS scheme outperforms HDM and Polar code with CRC-aided list decoding for short (32-bit) message transmission.
DOI：
10.1109/icc.2019.8761286
发表时间：
2018-11
期刊：
ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
影响因子：
--
作者：
Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan;Sewoong Oh;P. Viswanath
通讯作者：
Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan;Sewoong Oh;P. Viswanath
影响因子：
2.5
作者：
Rush, Cynthia;Greig, Adam;Venkataramanan, Ramji
通讯作者：
Venkataramanan, Ramji