Graph cut based clustering for cognitive radio ad hoc networks without common control channels

Graph cut based clustering for cognitive radio ad hoc networks without common control channels
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没有公共控制信道的认知无线电自组织网络的基于图割的聚类

DOI:
10.1007/s11276-016-1329-5
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发表时间:
2016-07
期刊:
影响因子:
3
通讯作者:
Zhiyong Wang
Zhiyong Wang
中科院分区:
计算机科学4区
文献类型:
--
作者:
Huyin Zhang;Ning Xu;Fang Xu;Zhiyong Wang

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分簇是大规模网络中提高路由和数据传输性能的有效工具。然而,在认知无线电自组织网络(CRAHN)中,由于动态频谱接入和盲信息环境,分簇设计是具有挑战性的。在本文中,我们提出了一种新的分布式分簇算法CRAHN,既不需要一个专用的公共控制信道(CCC),也不需要事先的拓扑信息。首先,提出了一种不依赖CCC的邻居发现协议来构造局部拓扑。然后,我们将网络建模为无向图,并将聚类过程表示为图切割问题。我们设计了一个基于mincut的启发式算法来逼近最优聚类解。在此基础上,我们还提出了一个同步协议来实现集群成员的全局一致性。最后,我们提出了一个积极的集群维护机制,以减少PU活动所造成的干扰。我们通过与其他聚类方法的比较来验证我们的工作。仿真结果表明,该方法通过根据频谱变化调整簇结构,降低了干扰,提高了网络效率。
Clustering is an efficient tool to improve the routing and data transmission performance in large scale networks. However, in cognitive radio ad hoc networks (CRAHNs), clustering design is challenging due to the dynamic spectrum access and the blind information environment. In this paper, we propose a novel distributed clustering algorithm for CRAHNs, where neither a dedicated common control channel (CCC) nor prior topology information is required. First, a neighbor discovery protocol without relying on CCC is proposed to construct the local topology. Then, we model the network as a undirected graph and formulate the clustering process as a graph cut problem. We design a mincut based heuristic algorithm to approximate the optimal clustering solution. After this, we also present a synchronize protocol to achieve the global consistency of cluster memberships. Finally, we propose a proactive cluster maintenance mechanism to reduce the interferences caused by PU activities. We validate our work through comparisons with other clustering methods. The simulation results show that, by adjusting the cluster structure according to the changing spectrum, the proposed method reduces the interference and improves the network efficiency.
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