Cyclostationary signal sensing algorithm based on principal component analysis and AdaBoost
Wei Qin · 2023
At present, with the development of 5G technology, the bandwidth requirement of data communication is increasing, which makes the limited spectrum resources become more and more tight. Based on the current situation of the utilization and allocation of radio spectrum resources, the unused frequency bands have become less and less, and the shortage of radio spectrum resources is very serious. In this paper, a spectrum sensing algorithm based on principal component analysis (PCA) and AdaBoost is proposed to solve the problem of low detection rate of main user signal in wireless channel environment. Firstly, we extract the feature parameters of the signal by using the cyclostationary PCA algorithm, obtain the principal components of the signal, generate the samples, and construct the sample set, then the AdaBoost algorithm is used to classify and detect the signals in the presence and absence of the main user. The simulation results show that the proposed algorithm has better classification and detection performance compared with the artificial neural network and the max-min eigenvalue algorithm under low signal-to-noise ratio, the sensing of primary user signal is realized effectively.