认知无线电中基于拟合优度的频谱盲检测算法研究

Blind spectrum sensing based on goodness of fit test for cognitive radio in noise of uncertain power

首先,提出噪声方差未知情况下基于t分布的Anderson-Darling的认知无线电频谱盲检测方法,通过计算接收到信道采样样本均值和方差比的分布函数与t分布函数之间的Anderson-darling距离,实现频谱检测。其次,提了基于特征函数的频谱盲检测算法,通过计算接收到的样本经验特征函数与已知特征函数的距离,判决信道中是否存在信号传输。最后,给出了衰落信道下,所提2种基于拟合优度的频谱盲检测算法的虚警概率和检测概率下界。理论和仿真表明,噪声方差未知情况下,所提2种频谱盲检测算法比传统的噪声方差已知时的能量检测法具有更好的性能。在低信噪比和小样本条件下,性能提高表现的尤其明显。

Based on t-distribution and Anderson-Darling test,the first,a method was proposed of spectrum sensing under assumptions that the noise variance was unknown and also the channel information was unknown.Indeed,the sensing was conducted based on Anderson-Darling distance between the distribution of ratio of sample’s mean to sample’s vari-ance and the t-distribution.The second,another sensing method was proposed,under the same assumptions as the above,based on Anderson-Darling distance between the empirical characteristic function and a known characteristic function.The third,lower bounds on detection probabilities of sensings above were given for a flat,slow fading channel.Theoreti-cal analysis and simulations show that the performances of proposed methods are much better than energy detector-based sensing with known noise variance,especially under an environment of a low signal to noise ration（SNR）,or small number of samples.