A Cooperative Spectrum Sensing Method Based on Clustering Algorithm and Signal Feature

Abstract

To solve the problem that the threshold is difficult to calculate in the spectrum sensing method of random matrix, this paper proposes a spectrum sensing method based on the combination of clustering algorithm and signal features. At the same time, in order to improve the performance of feature estimation and the detection performance under the condition of small number of cooperative users, a concept based on stochastic matrix splitting and reorganization is introduced to logically increase the number of cooperative users. In order to further obtain the information from the signal matrix and improve the feature accuracy, the signal perceived by each secondary user (SU) is decomposed into I and Q (IQ) components. Firstly, the signal matrix is split and reassembled and IQ decomposed. Then the covariance matrices of split matrix and matrix after IQ decomposition are calculated respectively and the corresponding eigenvalues are obtained. Then, the features are formed into a feature vector. Finally, the algorithm classifies these feature vector. The simulation experiments under different signal characteristics and different clustering algorithms show that the proposed method can effectively improve the performance of spectrum sensing.