用于高动态噪声背景的归一化谱协作感知算法

Normalized spectrum based cooperative spectrum sensing algorithm for high dynamic noise background

针对背景噪声电平在多维域上的高动态变化严重影响协作感知算法的性能这一问题,本文提出基于归一化谱的协作感知算法。该算法利用认知用户归一化谱的等增益平均作为检验统计量,依据傅里叶变换的渐进正态性和相互独立性计算功率谱的统计特性,推导出算法虚警概率和判决门限的闭式表达式。仿真表明:归一化谱协作感知算法对噪声电平的动态变化具有鲁棒性;相对于基于能量的大数判决硬协作感知算法和等增益软协作感知算法,本文算法具有更广泛的适用性。

As high dynamic ranges of background noise level in time,spatial and frequency domain seriously affect the performance of cooperative spectrum sensing algorithms,a novel cooperative spectrum sensing algorithm based on Normalized Spectrum（NS）is proposed.The local NS is uploaded to the fusion center,and the equal gain average of NS is treated as detection statistics to detect signals in the fusion center.It makes use of asymptotic normality and independence of Fourier transform to get the stochastic properties of Power Spectral Density（PSD）.The mathematical expression for probabilities of false alarm is derived.In accordance with the Neyman-Pearson criteria,the closed-form expression of decision threshold is calculated.The simulation results show that the NS algorithm is robust to dynamic ranges of background noise level.With respect to Majority Voting Hard Decision（MVHD）cooperative spectrum sensing algorithm and Equal Gain Soft Decision（EGSD）cooperative spectrum sensing algorithm based on energy,the NS algorithm has better performance and broader applications.