电子学报 ›› 2016, Vol. 44 ›› Issue (10): 2323-2329.DOI: 10.3969/j.issn.0372-2112.2016.10.006

• 学术论文 • 上一篇    下一篇

认知Ad Hoc网络中基于信道相似度的分簇算法研究

徐宁, 张沪寅, 王晶, 徐方, 汪志勇   

  1. 武汉大学计算机学院, 湖北武汉 430072
  • 收稿日期:2015-02-02 修回日期:2015-06-26 出版日期:2016-10-25 发布日期:2016-10-25
  • 通讯作者: 张沪寅
  • 作者简介:徐宁,男,1989年生于湖北十堰,博士生,主要研究方向为移动无线网络、认知AdHoc网络、上下文认知计算.E-mail:davidxn@whu.edu.cn
  • 基金资助:

    国家自然科学基金(No.61272454);高等学校博士学科点专项科研基金(No.20130141110022)

Channel Similarity Based Clustering Algorithm in Cognitive Ad Hoc Network

XU Ning, ZHANG Hu-yin, WANG Jing, XU Fang, WANG Zhi-yong   

  1. School of Computer, Wuhan University, Wuhan, Hubei 430072, China
  • Received:2015-02-02 Revised:2015-06-26 Online:2016-10-25 Published:2016-10-25

摘要:

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.

关键词: 认知Ad Hoc网络, 分簇算法, 信道相似度

Abstract:

As the traditional clustering algorithm cannot be applied to the cognitive ad hoc network for dynamic channels,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 algorithm.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.

Key words: cognitive Ad Hoc network, clustering, channel similarity

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