计算机科学 ›› 2011, Vol. 38 ›› Issue (11): 59-61.

• 计算机网络与信息安全 • 上一篇    下一篇

基于近邻传播的认知Ad-hoc网络分簇算法

张建照,姚富强,赵杭生,柳永祥,王凡   

  1. (解放军理工大学通信工程学院 南京210007)(总参第六十三研究所 南京210007)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(61072077) , 国家重大专项(2010ZX03006-002-O1) ,通信抗干扰技术国家级重点实验室基金项目(914000203020905)资助。

Clustering Algorithm for Cognitive Radio Ad-hoc Networks Based on Affinity Propagation

ZHANG Jian-zhao,YAO Fu-qiang,ZHAO Hang-sheng,LIU Yong-xiang,WANG Fan   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对认知Ad-hoc网络中可用信道动态异构和缺乏全网公共信道的特点,提出了一种基于限制消息交互次数的近部传播模型(Affinity Propagation, AP)的分簇算法。该算法通过网络中相邻节点间的消息交互和更新,在相部节点最多的信道上以可用信道最多的节点为簇首建立簇结构。为适应认知Ad-ho。网络环境的变化,降低分簇开销,算法限制AP消息的交互次数,实现了分簇算法的分布式快速收敛。仿真分析表明,算法降低了网络中的簇数目,提高了簇内平均可用信道和公共信道数目,从而为分布式频谱协作提供了高效的网络拓扑环境。

关键词: 认知Ad-hoc网络,分簇,近部传播模型,稳健性

Abstract: According to the dynamic heterogeneity of available channels and scarcity of global common channels in cognitive radio Ad-hoc networks(CRAHNs),a distributed clustering algorithm CRAP-CMI(Clustering Based on Affinity Propagation with Controlled Message Interchanges) was proposed. Based on the interchange and update of messages among neighboring nodes, cluster structure was constructed on the channels shared by most local neighbors with the nodes holding most available channels as cluster heads. To reduce the clustering overhead as well as adapt to the variadons in CRAHNs, CBAP-CMI refines the number of messages interchange and achieves rapid distributed clustering.The simulation results show that the proposed algorithm reduces the number of clusters and increases the average available channels for each link and the common channels in each cluster, providing an efficient topology for distributed spectrum cooperation in CRAHNs.

Key words: Cognitive radio Ad-hoc networks,Clustering,Affinity propagation,Robustness

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