认知无线电中一种感知节点集自适应选择算法

本文主要考虑认知网络中感知节点集的选择问题。联合谱感知技术虽然可以极大地提高认知系统的感知性能，但是随着参与感知的认知节点数目的增加，对系统资源的占用也会越来越多，使系统的传输效率下降。本文首先给出了认知网络中最优感知节点集的概念，接着分析了最优感知节点集的节点数目和平均接收信噪比所必须满足的条件，最后通过推导得到了在固定虚警概率条件下最优感知节点集的检测概率与它的节点数目和平均接收信噪比之间的关系表达式，并在此基础上提出了一种最优感知节点集的自适应选择算法。该算法不但能在认知网络中寻找最优感知节点集，同时还可以适应认知网络的动态拓扑变化。仿真结果证明了该算法的有效性。 

An Adaptive Sensors Set Selecting Algorithm in Cognitive Radio

The problem of optimal sensor set (OptSS) selection in cognitive radio networks is considered in this paper. Although the method of cooperative spectrum sensing can greatly improve the sensing performance， the consumption of system resources will increase as the number of cooperative sensors increases， therefore， the number of cooperative sensors to use is a compromise between sensing performance and consumption of system resources. To the best of our knowledge， this is the first effort to select OptSS for cooperative spectrum sensing in cognitive radio， under sensing performance requirements. Based on the expression for the probability of detection which is characterized as a function of the number of cooperative sensors and the global average receiving signal to noise ratio of OptSS， an optimal sensor set selecting algorithm is proposed. Simulation results demonstrate the effectiveness and reliability of the proposed algorithm.