基于扩散策略的实时分布式协作频谱检测算法

针对传统分布式协作频谱检测算法认知用户不能实时检测问题，该文提出基于扩散策略的实时分布式协作检测算法。算法利用各个节点的本地代价表示全局代价，通过最小化各个节点的代价使得全局代价最小。采用最速下降法，利用迭代方式计算各个节点检测量的最优估计值，得出估计值的理论稳态均值和方差，得出虚警概率、检测概率以及检测门限的封闭表达式。理论分析和实验结果表明，该算法能够有效解决分布式网络认知节点的实时检测问题，并具备快速学习和适应环境变化的能力。当虚警概率为0.01且检测概率达到0.9时，平均信噪比较平均共识和非实时扩散策略降低了约6 dB，能够实现在极低信噪比条件下的信号检测。

Real-time Distributed Cooperative Spectrum Detection Algorithm Based on Diffusion Strategy

Considering the problem of real-time distributed cooperative spectrum detection of cognitive users, a real-time distributed cooperative spectrum detection algorithm based on diffusion strategy is proposed. Global cost function can be approximated by an alternative localized cost that is amenable to distributed optimization. Each individual node optimizes this alternative cost via a steep-descent procedure that relies solely on interaction within the neighborhood of the node. The local estimate value can be calculated via the iteration procedure. A general model for analyzing the mean and variance of the estimates of the diffusion strategy is derived. The formulas of probability of detection, probability of false alarm and detection threshold are derived. Theoretical analysis and experimental results show that the proposed algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Compared with average consensus strategy and non-real-time diffusion strategy, the average SNR of the proposed algorithm reduces about 6 dB, while the Pfa below 0.01 and Pd reached to 0.9. The diffusion strategy can satisfy the signal detection in very low SNR.