采用T2统计量和拟合优度的盲频谱感知算法

针对低信噪比和采样数少时频谱感知性能差的问题，首先利用样本检验矩阵的样本特征构造T2统计量，再结合似然比准则构造新的检验统计量，最后推导了频谱空闲时检验统计量的概率密度函数，提出基于贝塔分布的盲频谱感知算法。但此算法在采样数较少时，检测性能较差，因此结合Anderson Darling准则，提出基于贝塔分布的拟合优度检验算法。在高斯信道和瑞利衰落信道下对所提算法进行仿真，并与已有盲频谱感知算法仿真结果进行对比，所提算法具有更好的检测性能，且不需要主用户信息，不用进行特征分解，不受噪声方差影响。 

Blind Spectrum Sensing Method Using T2 Statistic and Goodness of Fit

In response to the problem that the performance of the spectrum sensing is poor in the low signal-to-noise ratio and the sampling number, we construct new test statistic. Firstly, the T2 statistics is constructed by using the sample feature of the sample test matrix. Then according to likelihood ratio criterion, the new test statistic is contained. Finally, when the spectrum is idle, the probability density function of the test statistic is deduced. And the blind spectrum sensing algorithm based on the Beta distribution is proposed. However, when the number of samples is small, the detection performance is poor. Therefore, according to the Anderson-Darling criterion, a new algorithm based on the Beta distribution and goodness of fit is proposed. The proposed algorithms are simulated under AWGN channel and Rayleigh fading channel, and compared with the simulation results of the existing blind spectrum sensing algorithm. The proposed algorithm has better detection performance and do not need the main user information, no feature decomposition, no influence of noise variance.