基于迭代用户选择的合作频谱感知算法

针对认知无线电中经典频谱感知算法 (能量检测、算术几何平均、信号特征值子空间、最大特征值检测)存在不同程度缺陷的问题，为了进一步提高频谱感知算法的检测性能，提出了基于迭代用户选择(iterative user selection, IUS)的合作频谱感知算法。该算法先对参与合作感知的全部认知用户进行选择，然后在选出的部分用户频谱观测数据的基础上，生成全局检验统计量(global decision statistic, GTS)，以此做出授权用户(primary user, PU)信号是否存在的全局判决。仿真结果显示，在虚警概率保持不变的情况下，进行迭代用户选择后，合作频谱感知的检测概率要优于未进行用户选择时的算法。与经典频谱感知算法相比较， IUS的合作频谱感知算法不需要任何先验信息，且以较少的频谱观测数据达到较好的检测性能。

Cooperative spectrum sensing algorithm based on iterative users selection

In order to deal with the drawbacks of different classical spectrum sensing algorithms (e.g. energy detection, arithmetic to geometric mean, signal-subspace eigenvalue, and maximum eigenvalue detection) and improve detection performance of spectrum sensing algorithm, this paper proposes an iterative user selection (IUS) based cooperative spectrum sensing scheme for cognitive radios. The proposed IUS scheme firstly selects, among all the cooperative users monitoring the licensed spectrum simultaneously, the cognitive users to participate in the cooperative sensing procedure. Subsequently, based on the spectrum observations of the selected users, the fusion center generates the global decision statistic (GTS) to determine whether the PU signal exists or not. Simulation results show that under the criterion of constant false alarm probability, the proposed IUS aided cooperative sensing algorithm yields improved detection probability compared to those without IUS. Compared with the classical spectrum sensing algorithms, iterative users selection (IUS) does not require a priori knowledge of the primary user signal, the channel state information, and the noise power and achieve a better detection performance with fewer spectrum observation data.