基于扩散策略的分布式协作频谱检测

分布式认知网络中，认知用户通过与相邻用户交换检测量信息，达到全网一致，实现对主用户信号的检测。为了提高网络检测量信息的收敛速度，提出了一种基于扩散策略的分布式协作检测算法。在该算法中，将最大最小特征值检测算法的检测量作为交换的初始信息，构造自适应矩阵和融合矩阵作为加权因子对认知节点状态值进行迭代更新，实现全网认知用户检测量信息一致，各认知用户独自根据融合的检测量信息，进行最终的检测判决，确定主用户是否存在。仿真结果表明，该算法在网络收敛速度和检测性能上较共识策略和非合作检测有不同程度的提升。

Distributed cooperative spectrum detection based on diffusion strategy

In distributed cognitive radio networks(CRN),the secondary users(SUs) achieve the information consensus of the whole network through exchanging the test statistic information with neighbors. For improving the convergence speed of the test statistic information over the network,a distributed cooperative spectrum detection algorithm based on diffusion strategy is proposed. The test statistic of the Maximum-Minimum Eigenvalue(MME) algorithm is regarded as the initial exchanging information. Adaptive and combination matrix,as the weighted factor,are introduced to update the state information of cognitive nodes to obtain the same convergence value,which is regarded as the information to make the final decision to determine whether the primary user(PU) exists. Simulation results show that the algorithm can improve the converging speed and detection performance of the network compared with the consensus strategy and the non-cooperative strategy.