A Recursive Frequency Smoothing Method of Cyclic Spectrum Density Estimation

Cyclic spectrum density estimation plays a significant role in cyclostationary signals analysis. Generally, the methods of cyclic spectrum density estimation are mainly focus in time-domain, such as FFT Accumulation Method (FAM) and Strip Spectral Correlation Algorithm (SSCA). In this paper, based on the principle of frequency smoothing methods, an improved method in frequency-domain was proposed, which could reduce the computational cost greatly by using recursion. The recursive method was based on the fact that the range difference of smoothing window was small when window slid. In other words, we could get a new point of cyclic spectrum density just by computing the complex conjugate products of newly coming into window. In addition, the simulation results were given at last, which showed the good performance of our proposed method.

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System

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.