中圖分類號(hào)：TN92 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223583 中文引用格式： 蔚淦丞,，廖明軍,，劉俊杰，等. 基于CNN和GRU的高階調(diào)制自動(dòng)編碼器研究[J]. 電子技術(shù)應(yīng)用,，2023,，49(5)：41-46. 英文引用格式： Yu Gancheng，Liao Mingjun,，Liu Junjie,，et al. High order modulation autoencoder based on CNN and GRU[J]. Application of Electronic Technique，2023,，49(5)：41-46.
High order modulation autoencoder based on CNN and GRU
(1.School of Communication and Information Engineering， Chongqing University of Posts and Telecommunications,， Chongqing 400065,， China； 2.Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China,， Chongqing 400065,， China； 3.Chongqing Key Laboratory of Ubiquitous Sensing and Networking,， Chongqing 400065,， China)
Abstract： Autoencoder (AE) based on deep learning is a new method to replace traditional communication transmitter and receiver. This paper proposes an autoencoder based on Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU), which integrates constellation mapping and channel coding. Specifically, this paper designs a parallel CNN structure and segment the input bitstream for one-hot encoding, which has two advantages:(1) Compared with the original one-hot encoding, the dimension of the input data is reduced; (2) The features of the data are not too sparse, which allows the network to converge faster and better. In addition, the GRU is introduced for channel coding. The proposed model can be applied to high-order modulation such as 4096QAM signal, and has better performance than traditional methods under both added white Gaussian noise (AWGN) channels and Rayleigh channels.
