Classification Method for Chirp Spread Spectrum Communication Formats Based on Multi-Feature Fusion
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摘要: 自动调制分类(AMC)在频谱监测和认知无线电中具有重要意义。近年来，Chirp扩频通信(CSS)由于其良好的抗干扰能力和稳健性得到了较大发展，但是对CSS信号的AMC方法却鲜有研究。针对这种情况，该文提出了一种基于多特征融合(MFF)的CSS信号调制分类方法，利用频谱和时频图特征融合学习并引入注意力模块来实现CSS信号调制识别。对11类CSS信号调制样式的仿真实验结果表明，该方法有优越的识别性能。Abstract: Automatic Modulation Classification (AMC) is essential for spectrum monitoring and cognitive radio. The Chirp Spread Spectrum (CSS) communication scheme could be developed remarkably due to its good anti-interference ability and robustness. However, research on the AMC of the CSS communication scheme is limited.Therefore, this paper proposes a CSS signal modulation classification method based on Multi-Feature Fusion (MFF) to enhance its recognition accuracy. This method which leverages spectrum and time-frequency map feature fusion learning and incorporates an attention module. The results of 11 types of CSS formats demonstrate that the proposed scheme exhibits superior recognition performance.
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表 1 1D-CNN模型的层数和每层的激活函数和输出维度
序号层名称 层结构参数 输入向量 维度:1×512 Conv1D+ReLU 输出维度:64×512 Dropout 正则化 丢弃率0.5 Conv1D+ReLU 输出维度:64×512 Dropout 正则化 丢弃率0.5 Conv1D+ReLU 输出维度:64×512 Dropout 正则化 丢弃率0.5 Flatten 输出维度:1×8192 Fc 输出维度:1×256 
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表 2 ResNet18模型参数描述
序号层名称 层结构参数 输入向量 维度:3×64×512 Conv2D+ReLU 输出通道:64; size:(3,3); stride:1 ResBlock1 输出通道:128; size:(3,3); stride:2 ResBlock2 输出通道:256; size:(3,3); stride:2 ResBlock3 输出通道:512; size:(3,3); stride:2 ResBlock4 输出通道:512; size:(3,3); stride:2 AvgPool 输出维度:1×256
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