基于噪声不确定性的双门限协作频谱感知算法

传统的双门限协作频谱感知算法忽略了两门限之间认知用户的本地感知信息,而利用这部分信息可以进一步提高认知无线电系统的感知性能。为此,在等增益合并的基础上,提出一种顺序自适应分步合并算法。两门限之间的认知用户根据接收信噪比分步上传本地感知信息,融合中心自适应地调整参与协作的认知用户数,以减小系统的上传数据开销。推导在Rayleigh衰落信道条件下,认知用户上传数据的平均比特数以及全局检测概率的闭式表达式。理论分析和仿真结果表明,该算法能以较小的数据开销获得较优的协作感知性能。

Double-threshold Cooperative Spectrum Sensing Algorithm Based on Noise Uncertainty

Traditional double-threshold cooperative spectrum sensing algorithms ignore the local sensing information of Cognitive Radio（CR） users between the two thresholds. However, using such information can further advance the sensing performance of CR system, so this paper puts forward a sequential adaptive step combination algorithm based on equal gain combining. The CR users between the two thresholds upload their local sensing information step by step with descending order in accordance with the received Signal-to-noise Ratio（SNR）, the Fusion Center（FC） adaptively adjusts the number of CR users participated in the collaboration to reduce the data overhead in CR networks. The formulae of this algorithm upload average bits and detection probability of cognitive users over Rayleigh fading channel conditions are deduced. Theoretical analysis and simulation results show that this algorithm can obtain better cooperative sensing performance with relatively fewer data overhead.