改进双门限能量检测分簇协作频谱感知算法

A Clustering Cooperative Spectrum Sensing Algorithm Based on Improved Double-Threshold Energy Detection

针对传统频谱感知算法在低信噪比环境下检测概率较低的问题,提出改进双门限能量检测分簇协作频谱感知算法。算法将感知节点分簇,簇间信息采用OR准则硬融合,簇内双门限外节点发送1 bit判决结果进行硬融合,双门限内节点发送能量值和信噪比值进行加权软融合。加权软融合阶段构造检测概率与节点权重系数函数,利用压缩因子粒子群算法选择权重系数进行函数寻优,以最大化检测概率。仿真结果表明,在低信噪比和不同感知用户数目环境下,算法仍具有较高的检测概率。

In order to solve the problem of low detection probability in the traditional spectrum sensing algorithm under low SNR environment, a clustering cooperative spectrum sensing algorithm based on improved double-threshold energy detection was proposed. The algorithm divided the sensing nodes into clusters. ＂OR＂ rule was used to the information between the clusters for hard fusion. Inner the clusters, to the nodes outside dual-threshold, a 1 bit decision result was transmitted for hard fusion;and to the nodes inside dual-threshold, energy values and SNRs were transmitted to implement weighted soft fusion. The detection probability and the node weight coefficient function were constructed under weighted soft fusion, and particle swarm optimization algorithm with compression factor was used to further optimize the function and maximize the detection probability. The simulation results verified that the algorithm has good detection probability under low SNRs and different number of users.