CNN-Transformer轻量级智能调制识别算法

Algorithm for recognition of lightweight intelligent modulation based on the CNN-transformer networks

现有基于深度学习的调制识别方法,当存在噪声及不确定信道干扰影响时识别率较低,同时由于模型参数量大,难以直接应用到移动终端。针对该问题,提出一种基于卷积神经网络(CNN)和Transformer的轻量级智能调制识别方法。该方法首先利用卷积神经网络对信号进行局部信息特征提取,然后利用卷积神经网络通道注意力和Transformer时域注意力模块分别从信号的通道和时域两个维度关注最有利于识别的特征,降低信道或噪声等的影响,以提升识别率。所提方法可以适应多种信号表征,如原始IQ信号、幅度相位信号及变换域特征。仿真表明,在RadioML2016.10b数据集上,相较现有基于卷积神经网络的方法,所提方法的平均识别率提升了约8%~12%,相比基于残差神经网络和长短时记忆网络的方法,参数量降低了约90%~92%,计算量降低了约83%~93%。实验结果验证了所提方法增加模型分类精度的同时,有效地降低了模型的参数量和计算量。

Existing modulation recognition methods based on deep learning have the problems of low recognition accuracy under the influence of noise and uncertain channel interference,and are difficult to apply to mobile terminals due to a large number of parameters.This paper proposes a lightweight modulation recognition method based on the Convolutional Neural Network(CNN)and Transformer to solve the above problems.In order to improve the accuracy,the CNN is first used to extract the local features of the signal.Then,the CNN-based channel attention and Transformer-based temporal attention modules are used to focus on the features that are most conducive to recognition from the two dimensions of the signal channel and time domain,respectively,while reducing the impact of the channel,noise,etc.The proposed method can be applied to a variety of signal representations,such as raw IQ signals,amplitude-phase signals,and transform domain features.Simulation shows that on the RadioML2016.10b dataset,compared with the existing convolutional network methods,the average recognition accuracy of the proposed method is increased by 8%~12%.Compared with the methods based on the residual neural network and long-term memory network,the number of parameters is reduced by 90%~92%,and the amount of calculation is reduced by about 83%~93%.Experimental results show that the proposed method can improve the accuracy of model classification while effectively reducing the number of parameters and the amount of calculation.