太赫兹科学与电子信息学报2021，Vol.19Issue(2)：235-243,9.DOI:10.11805/TKYDA2020034
基于深度LSTM辅助卷积网络的新型自动调制分类
A novel efficient automatic modulation classification algorithm using deep LSTM aided convolutional networks
吴楠 1谷万博 1王旭东1
作者信息
- 1. 大连海事大学 信息科学技术学院,辽宁 大连 116023
- 折叠
摘要
Abstract
Automatic modulation classifications would play an essential part in wireless spectrum anomaly detection and radio environment awareness. With the breakthrough in deep learning algorithms, this issue can be solved with unprecedented precision and effectiveness by using neural networks. Therefore, a novel neural network termed as Long short-term Convolutional Deep Neural Network(LCDNN) is proposed, which creatively combines the complimentary merits of Long Short-Term Memory(LSTM), Convolutional Neural Network(CNN) and deep network architectures. This model directly learns from raw time domain amplitude and phase samples in training dataset without requiring human engineered features. Simulation results show that the proposed model yields a classification accuracy of 93.5％ at high SNRs. Further, the noise sensitivity of the proposed LCDNN model is examined and it is showed that LCDNN can outperform existing baseline models across a range of SNRs. Finally, in order to reduce the computational complexity of the LCDNN model, a 'compact' LCDNN model is proposed, which achieves the state-of-the-art classification performance with only 0.6％ parameters of the original LCDNN model.关键词
调制分类/深度学习/长短期卷积深度神经网络/卷积神经网络Key words
modulation classification/deep learning/long short-term Convolutional Deep Neural Network/convolutional neural network分类
信息技术与安全科学引用本文复制引用
吴楠,谷万博,王旭东..基于深度LSTM辅助卷积网络的新型自动调制分类[J].太赫兹科学与电子信息学报,2021,19(2):235-243,9.
