低信噪比条件下基于 Pietra-Ricci 指数和 SVM 的协作式盲频谱感知算法

To solve the problem of low spectrum recognition rate under low signal-to-noise ratios (SNRs) conditions in cognitive radio spectrum sensing, the blind spectrum sensing algorithm based on Pietra-Ricci Index (PRI) and Support Vector Machine (SVM) was proposed. The PRI sensing decision metric was constructed by sampling the covariance matrix. The SVM was trained by the calibrated feature samples to obtain the optimal classification model for spectrum occupancy states. The PRI was utilized as feature quantity to effectively characterize the variation characteristics of the received signal. By introducing kernel function, the signal feature space was mapped to the high-dimensional space, which was expected to facilitate sample discrimination. The spectrum sensing classifier combining PRI and SVM was constructed. Using PRI as decision metric, the algorithm flow and complexity analysis were provided, and the algorithm was simulated and analyzed. The results show that the new algorithm can accurately classify the user signals and noise under low SNRs conditions, and it achieves lower computational complexity compared to similar algorithms. Compared to the existing algorithms, for the false alarm probability of 0.1, the detection probability reaches 89.4% by the proposed algorithm, which is increased by 20.0% than that by Cholesky decomposition-based method with only 69.4%. The proposed algorithm can significantly enhance the accuracy of primary user signal identification in cognitive radio systems.