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Automatic Modulation Classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD

机译:利用卷积神经网络融合SPWVD和BJD的自动调制分类

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摘要

Automatic modulation classification (AMC) is becoming increasingly important in spectrum monitoring and cognitive radio. However, most existing modulation classification algorithms neglect the complementarities between different features and the importance of features fusion. To remedy these flaws, this paper presents a scheme of features fusion for AMC using convolutional neural network (CNN). The approach attempts to fuse different images and handcrafted features of signals to obtain more discriminating features. First, eight handcrafted features and different images features are both extracted. In the latter, signals are converted into two kinds of time-frequency images by smooth pseudo-wigner-vine distribution and Born Jordan distribution, and a fine-tuned CNN model is utilized to extract image features. Second, the joint features are formed by combination of each of image and handcrafted features, and a multimodality fusion model is applied to fuse the joint features to yield further improvement. Finally, simulation results reveal the superior performance of the proposed scheme. It is worth mentioning that the classification accuracy can reach 92.5% with signal-to-noise ratio at -4 dB.
机译:自动调制分类(AMC)在频谱监视和认知无线电中变得越来越重要。但是,大多数现有的调制分类算法都忽略了不同特征之间的互补性以及特征融合的重要性。为了弥补这些缺陷,本文提出了一种使用卷积神经网络(CNN)的AMC特征融合方案。该方法试图融合不同的图像和信号的手工特征以获得更多区分特征。首先,提取八个手工制作的特征和不同的图像特征。在后者中,信号通过平滑的伪维格纳-维恩分布和伯恩·乔丹分布转换为两种时频图像,并利用微调的CNN模型提取图像特征。其次,将图像和手工特征中的每一个结合起来形成关节特征,并应用多模态融合模型融合关节特征以产生进一步的改进。最后,仿真结果表明了该方案的优越性能。值得一提的是,在-4 dB的信噪比下,分类精度可以达到92.5%。

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