噪声功率不确定下协同频谱感知最优融合算法

研究了噪声功率波动性对单点和协同频谱感知性能地影响。分析了在此环境下,单点基站的检测概率与采样点个数的关系,并得出当信噪比不小于噪声功率波动系数的两倍时,基站仍可正常检测。在协同网络中,给出了此环境下数据融合方法的最优权重设定。理论分析与实验表明,相比于传统方法,文中所提最优数据融合算法在各种信噪比环境下性能均较优,尤其在各基站信噪比差异较大时,性能更为显著。

Optimal Fusion Method for Cooperative Spectrum Sensing Under Noise Power Uncertainty

The performance of spectrum sensing investigated under the noise power fluctuation circumstances. Firstly, the lowest signal noise ratio （SNR） of which the base user can work normally is analyzed. And, when the SNR is larger than the twice parameter of noise power fluctuation, the reliable detection information can be attained. Moreover, under noise power fluctuation environments, the proposed for the proposed cooperative spectrum sensing. Theoretical analysis and experiments show data fusion scheme can be attained in various SNR situations, especially optimal fusion method can be that the better performance of in the larger difference of SNR environments.