**Xiaoyong Sun, Shaojing Su, Zhen Zuo \*, Xiaojun Guo and Xiaopeng Tan**

College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China; sunxiaoyong14@nudt.edu.cn (X.S.); susj-5@163.com (S.S.); jeanakin@nudt.edu.cn (X.G.); tanxiaopeng14@nudt.edu.cn (X.T.)

**\*** Correspondence: z.zuo@nudt.edu.cn; Tel.: +86-731-84573353

Received: 15 January 2020; Accepted: 3 March 2020; Published: 6 March 2020

**Abstract:** In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.

**Keywords:** modulation classification; high-order cumulant; cyclic spectrum; compressed sensing; decision tree–support vector machine
