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

In order to improve the spectrum sensing performance, we propose a cooperative spectrum sensing method based on a feature and clustering algorithm in the case of a small number of secondary users participating in cooperative spectrum sensing. This method introduces order decomposition and recombination and interval decomposition and recombination based on stochastic matrix, which can increases the secondary users logically. Firstly, the signal matrix collected by the secondary users is split and recombined, and the corresponding covariance matrix are calculated respectively to obtain the corresponding signal features. Based on these features, we construct them as a feature vector. Further, we will use the clustering algorithm to train and perform spectrum sensing based on the trained classifier. In the experimental and results analysis section, the method described in this paper was simulated and the experimental results were further analyzed.

I. Introduction

Classical spectrum sensing methods include energy detection, matched filtering and detection of cyclic features [1]. Energy detection is the simplest spectrum sensing algorithm as it has a low computational complexity. Therefore, this method is widely used. However, the algorithm is susceptible to noise uncertainty and a low signal-to-noise ratio (SNR), which will greatly reduce its detection performance [2].