电讯技术2018，Vol.58Issue(6)：702-707,6.DOI:10.3969/j.issn.1001-893x.2018.06.014
分离通道联合卷积神经网络的自动调制识别
Automatic Modulation Recognition Based on Separate Channel Combined Convolutional Neural Networks
郭有为 1蒋鸿宇 2周劼 1苏建中1
作者信息
- 1. 中国工程物理研究院电子工程研究所,四川 绵阳621999
- 2. 中国工程物理研究院研究生院,四川 绵阳621999
- 折叠
摘要
Abstract
An automatic modulation recognition algorithm based on Separate Channel Combined Convolu-tional Neural Network( SCC-CNN) is proposed to solve the mass computation of feature extraction in con-ventional automatic modulation recognition methods. The algorithm can extract features from multi-channel and separate channels of time domain data respectively by combining the convolutional neural network of deep learning,and then different signals can be classified by combined features. The simulation results re-veal that compared with the methods based on CNN the proposed algorithm can improve the accuracy under high signal-to-noise ratio(SNR) more than 7% and 18% on two different datasets;furthermore,high order modulation recognition performance is improved more than 3 dB compared with the methods based on fea-ture extraction.关键词
时域信号/自动调制识别/深度学习/卷积神经网络/分离通道Key words
time domain signal/automatic modulation recognition/deep learning/convolutional neural net-work/separate channel分类
信息技术与安全科学引用本文复制引用
郭有为,蒋鸿宇,周劼,苏建中..分离通道联合卷积神经网络的自动调制识别[J].电讯技术,2018,58(6):702-707,6.
