Wei Yangjie, Fang Shiliang, Wang Xiaoyan
Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China.
Entropy (Basel). 2019 Jul 30;21(8):745. doi: 10.3390/e21080745.
Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.
由于数字通信信号在无线电和水声系统中被广泛使用，这些信号的调制分类在各种军事和民用应用中变得越来越重要。然而，由于不利的信道传输特性和低信噪比（SNR），通信信号的调制分类极具挑战性。本文提出了一种基于混合特征、循环平稳性和信息熵的支持向量机（SVM）对数字通信信号进行自动调制分类的新方法。在该方法中，通过结合循环平稳性和熵的理论，基于现有的信号特征，我们提出了另外三个新特征来辅助数字通信信号的分类，它们分别是循环频率不为零时归一化循环谱的最大值、循环谱的香农熵和循环谱的雷尼熵。由于这些新特征不需要任何先验信息且具有很强的抗噪能力，它们非常适合通信信号的识别。最后，设计了一对一的SVM作为分类器。仿真结果表明，所提方法在分类性能和噪声容限方面优于现有方法。
