一种新的循环谱统计量频谱感知算法

为了提高认知无线电中感知用户对授权用户（主用户）的检测概率，提出一种新的基于循环谱统计量的主用户信号检测算法．该算法利用循环谱离散频域平滑方法，对信号的循环谱在循环频率处建立合理的检测统计量，然后求解得到对应不同虚警概率的判决门限，以使感知用户能检测信噪比更低的授权用户信号．该算法具有检测结构简单，易实现的特点．仿真结果表明，在高斯白噪声信道中，虚警概率为0．1，频域平滑累积次数为255，在信噪比为-16dB的条件下，与Dandawate-Giannakis（DG）方法相比，本文算法的检测概率提高23％．

A New Spectrum Sensing Method of Cyclic Spectrum Statistics

To increase the detection probability of sensing users for licenced users（primary signals） in the cognitive radio, it puts forward a new primary users signal method based on cyclic spectrum statistics. By using discrete frequency smoothing of cyclic spectrum, this algorithm establishes the reasonable detection statistics on the cyclic frequency of signal cyclic spectrum, then solves and obtains the decision threshold correspond to different false alarm probability, as a result, making the sensing users can detect licenced users signal under lower signal in noise ratio（SNR）. This algorithm has the characteristics of simple detection structure and implementation. The simulation results indicates that, when in additive Gaussian white noise（AGWN）, the false alarm probability is 0.1, the number of frequency smoothing is 255, SNR is- 16 dB, compared with Dandawate-Giannakis（DG） method, the detection probability of this algorithm increases 23 %.