论文标题
无线通信网络的数据和知识双驱动自动调制识别
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
论文作者
论文摘要
自动调制分类在无线通信网络中至关重要。基于深度学习的自动调制分类方案由于卓越的准确性引起了广泛的关注。但是，数据驱动的方法取决于大量训练样本，并且在低信噪无线电（SNR）中的分类精度很差。为了解决这些问题，通过利用不同调制的属性特征，提出了一种基于射频机器学习的新型数据和知识双驱动的自动调制分类方案。视觉模型用于提取视觉特征。属性学习模型用于学习属性语义表示。提出了转换模型将属性表示转换为视觉空间。广泛的仿真结果表明，我们提出的自动调制分类方案可以在分类准确性方面比基准方案获得更好的性能，尤其是在低SNR中。此外，与其他传统方案相比，使用我们所提出的方案减少了高阶调制之间的混乱。
Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.
