认知Ad Hoc网络中基于信道相似度的分簇算法研究

摘要

As the traditional clustering algorithm cannot be applied to the cognitive ad hoc network for dynamic chan-nels,a distributed clustering algorithm based on the similarity of channels has been proposed.Firstly the channel similarity between nodes will be calculated and the probability of a node within the cluster will be estimated using an adapted EM algo-rithm.Then by using minimum cut algorithm in graph theory,the optimal clustering results will be obtained with maximum similarity within a cluster and minimum similarity between clusters.Finally,a coordination mechanism to synchronize the global clustering information has been proposed.Throughout,these processes are evenly distributed,without relying on a common control channel.The simulation results show that the proposed algorithm can change the cluster structure according to the dynamic nature of channels,increase the intra-cluster common channels,and effectively reduce inter-cluster common channels to lower the interference.%针对传统分簇算法无法适用于信道动态变化的认知Ad Hoc网络，提出了一种基于信道相似度的分布式分簇算法。首先计算节点间的信道相似度，利用改进的EM算法估计节点属于不同簇的概率，再结合图的最小割算法取得最优的分簇结果。算法既最大化簇内相似度，也最小化簇间相似度。最后，提出了一个协调机制，可以同步全局的分簇信息。整个过程完全分布式运行，并且无需依赖公共控制信道。仿真结果表明，算法能够根据信道变化，动态地调整分簇结构，提高簇内公共信道数量。与此同时，算法还能有效减少簇间公共信道，降低簇间通信干扰。