认知无线网络中基于Cholesky分解的统计协方差频谱检测算法

针对认知无线网络中协方差检测算法均通过渐进方法得到性能参数的缺点，提出了改进的cholesky的协方差盲检测算法，利用RMT(random matrix theory)理论，推导了非渐进条件下该算法性能参数的数学表达式。所提算法无需PU信号的先验信息和信道条件信息，对不确定噪声具有很强的适应能力。理论分析和仿真证明，性能参数表达式正确，所提算法相对于其他协方差盲检测算法，性能有了一定的提升。

Statistical covariance blind detection algorithm based on cholesky factorization in cognitive radio network

As the blind covariance detection algorithm has the shortcoming that the performance parameters are obtained using non-asymptotic method,a new blind detection algorithm was presented using cholesky factorization.Utilizing random matrix theory,the performance parameters was derived using non-asymptotic method.The proposed method overcomes the noise uncertainty problem and performs well without information about the channel,primary user and noise.Numerical simulation results demonstrate that the performance parameters expressions are correct and the new detector outperforms the other blind covariance detectors.