基于特征融合的通信信号自动调制识别

Automatic Modulation Recognition of Communication Signals Based on Feature Fusion

针对现有通信调制识别技术方法存在的识别类型少、整体识别率低等缺点,提出了一种基于特征融合的通信信号自动调制识别方法空洞卷积注意力长短时神经网络(atrous convolutional block attention module CNN LSTM neural net,ACCLNN)。首先,采用空洞空间金字塔池化(atrous spatial pyramid pooling,ASPP)捕捉更多的不同尺度的输入信息,通过并联卷积神经网络(convolutional neural network,CNN)提取空间特征,并引入CBAM(convolutional block attention module)注意力模块增强数据关键部分的特征表示,提取低层次特征。然后,在长短时记忆网络(long short term memory,LSTM)提取时间特征后,引入自我注意力模块进行深度特征提取。最后,对低层次特征和深层次特征进行融合,完成特征提取和映射。经过实验验证,该方法在低信噪比环境下也能有效识别通信信号类别,总体识别率超过93%。

In response to the limitations observed in existing communication modulation recognition technologies,including a restricted number of recognition types and a suboptimal overall recognition rate,an automatic modulation recognition method for communication signals based on feature fusion(ACCLNN)was proposed.Initially,the method leveraged ASPP to capture diverse input information across different scales.Spatial features were then extracted through a parallel convolutional neural network(CNN),incorporating the CBAM attention module to enhance feature representation in critical data areas and extract low-level features.Subsequently,a long short-term memory network(LSTM)was employed for temporal feature extraction,accompanied by the introduction of a self-attention module for profound feature extraction.The final step involved fusing low-level features and deep features to achieve comprehensive feature extraction and mapping.Experimental results demonstrate the efficacy of the proposed method in identifying communication signal categories in low signal-to-noise ratio(SNR)environments,achieving an overall recognition rate exceeding 93%.