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.
Highlights
As one of the booming communication technologies in the information era, modulation classification (MC) technology [1] has a very important application value in the field of wireless communication
We propose a new modulation classification method that combines high-order cumulants and cyclic spectrum feature extraction methods with a decision tree–support vector machine (DT–SVM) classifier
Through the above calculation and analysis of the compressed values of the feature parameters based on compressed sensing (CS), the feature parameters are input into the decision tree–support vector machine (DT–SVM)
Summary
As one of the booming communication technologies in the information era, modulation classification (MC) technology [1] has a very important application value in the field of wireless communication It can play an important role in communication investigation, electronic countermeasures, signal authentication, interference identification, spectrum management, etc. We propose a new modulation classification method that combines high-order cumulants and cyclic spectrum feature extraction methods with a decision tree–support vector machine (DT–SVM) classifier. The sparse signal reconstruction is reconstructing the original signal from the signal observations with high probability by solving the non-linear optimization problem, which breaks through the limitations of the traditional Shannon–Nyquist sampling theorem and solves the performance requirements of a sampling system when processing cognitive radio signal.
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