高斯混合噪声环境中基于分数低阶矩的频谱感知算法研究

传统的频谱感知算法因非高斯噪声的干扰，其检测性能严重退化。为抑制实际通信环境中非高斯噪声的干扰，本文提出了一种基于分数低阶矩的空闲频谱检测方法，该方法不需要主用户信号、通信信道和噪声的先验信息。本文采用高斯混合分布拟合非高斯噪声环境，根据中心极限定理及广义二项式定理推导出信道为无衰落和Nakagami衰落时基于分数低阶矩感知算法的检测概率和虚警概率。理论分析和仿真结果表明，在非高斯噪声环境中，基于分数低阶矩感知算法的检测性能明显优于传统的能量检测算法，且采用多天线技术有助于进一步提高感知性能和频率资源的利用率。 

Spectrum Sensing for Cognitive Radio Under Gaussian Mixture Noise Using Fractional Lower Order Moments

The performance of the traditional spectrum sensing methods based on the second order statistics (noise environment is assumed to be a Gaussian distribution) may degrade severely due to the heavy tail characteristics of the probability density function (PDF) of the non-Gaussian noise in the actual environment. To this end, we propose a novel fractional lower order moments based detector, which does not require any a priori knowledge about the primary user (PU), channels and noise. The non-Gaussian environment is modeled by the Gaussian mixture distribution. For both non-fading and Nakagami fading communication channels between the transmitter of the PU and the multiple antennas of the second user (SU), its detection performance in terms of the probabilities of detection and false alarm are derived by using the central limit theorem and the generalized binomial theorem. Analytical and computer simulation results show that the fractional lower order moments based detector can significantly enhance the spectrum sensing performance over the conventional energy detection with non-fading as well as fading channels in the non-Gaussian noise environments, and the multi-antenna diversity scheme exhibits better detection performance and higher utilization of spectrum resources.