Spectrum sensing algorithm based on image K-means clustering analysis

 In recent years, cooperative spectrum sensing algorithms based on energy detection are widely used in the field of spectrum sensing. Because the method is computationally inspected, the decision threshold of energy detection is greatly affected by noise and limited by the number of cognitive users. In order to solve this problem, this paper proposes a spectrum sensing method based on image K-means clustering. In this method, two cognitive signal states of the presence or absence of the main user signal are mapped into images, and feature vectors are extracted by image processing, and then the K-means clustering algorithm is used to train the feature vectors to obtain a classification model. Finally, the trained classification model is used to detect the unknown signal to achieve spectrum sensing. The simulation results show that the spectrum sensing algorithm based on image classification proposed in this paper is superior to the energy detection sensing algorithm and cooperative spectrum sensing algorithm in detection performance, and the effect is more obvious at low SNR and low false alarm probability.