江苏大学学报（自然科学版）2025，Vol.46Issue(3)：316-322,7.DOI:10.3969/j.issn.1671-7775.2025.03.009
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
Cooperative blind spectrum sensing algorithm based on Pietra-Ricci index and SVM at low SNRs
摘要
Abstract
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.关键词
认知无线电/盲频谱感知/低信噪比/Pietra-Ricci指数(PRI)/SVM/协作式/协方差矩阵/决策分类Key words
cognitive radio/blind spectrum sensing/low SNRs/Pietra-Ricci index(PRI)/SVM/collaborative/covariance matrix/decision classification分类
信息技术与安全科学引用本文复制引用
田欣鑫,雷可君,潘小萍,张淞,谭宇豪,杨喜..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[J].江苏大学学报（自然科学版）,2025,46(3):316-322,7.基金项目
国家自然科学基金资助项目(61861019,62161012) （61861019,62161012）
湖南省教育厅科学研究项目(21A0335) （21A0335）
国家级大学生创新训练项目(s202010531009,202110531029) （s202010531009,202110531029）
