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

自动调制分类在无线频谱异常检测和无线电环境感知中将发挥重要作用。随着深度学习算法的突破,调制分类任务可利用神经网络达到前所未有的高分类精确度。文中提出了一种新颖的神经网络,称为长短期卷积深度神经网络(LCDNN)。该网络创造性地结合了长短期记忆网络(LSTM)、卷积神经网络(CNN)和深度网络体系结构的优点。该模型无需人工提取特征,并可直接从训练数据集中的原始时域幅度和相位样本中学习。仿真结果表明,该模型在高信噪比下的分类精确度达到93.5%。此外,对模型的噪声敏感性进行了研究,并证明在一定信噪比范围内,LCDNN模型较现有的基础模型在噪声破坏方面更具弹性。最后,为降低模型计算的复杂度,提出了一种结构更为简洁的LCDNN模型,该模型仅使用原始LCDNN模型的0.6%的参数即可实现高精确度的分类准确率。

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-theart classification performance with only 0.6%parameters of the original LCDNN model.