认知无线电中基于相似性的自适应分簇算法

A Comparability Based Adaptive Clustering Algorithm in Cognitive Radio Network

针对认知无线电网络(CRN)中可用信道实时变化的特点,运用图形理论提出一种基于相似性的自适应分簇(CBAC)算法.该算法是以用户可用信道的相似性为基础,结合用户的移动性,通过计算节点权值实现CRN的优化分簇.仿真分析证明,CBAC算法提高了系统的链路平均可用信道数,相比传统的分簇算法,能提高频谱的利用效率.

According to the characteristic of real-time changes of available channels in cognitive radio network, a new comparability based adaptive clustering algorithm （CBAC） in application of graph theory is proposed. Based on the comparability of users＇ available channels and the consideration of mobility of cognitive radio users, the algorithm optimizes the clustering result in cognitive radio network via computing the node＇s weight. Experiments show that the CBAC algorithm increases the number of the link＇s average available channels and has higher spectrum utilization rate and lower communication overhead than that of traditional clustering algorithms.