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
复制标题
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
影响因子:
3
通讯作者:
Zhiyong Wang
中科院分区:
文献类型:
--
作者:
Huyin Zhang;Ning Xu;Fang Xu;Zhiyong Wang
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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影响因子:
6.8
作者:
H. Salameh;Mohammad F. El-Attar
通讯作者:
H. Salameh;Mohammad F. El-Attar
影响因子:
2.3
作者:
Rauniyar, Ashish;Shin, Soo Young
通讯作者:
Shin, Soo Young
DOI:
10.1002/wcm.481
发表时间:
2007
期刊:
Wirel. Commun. Mob. Comput.
影响因子:
--
作者:
Jun Zhao;Haitao Zheng;Guang-Hua Yang
通讯作者:
Jun Zhao;Haitao Zheng;Guang-Hua Yang
DOI:
10.1109/sahcn.2009.5168974
发表时间:
2009-06
期刊:
2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks
影响因子:
--
作者:
Loukas Lazos;Sisi Liu;M. Krunz
通讯作者:
Loukas Lazos;Sisi Liu;M. Krunz
影响因子:
2.3
作者:
Jong-Hong Park;Yeonghun Nam;Jong‐Moon Chung
通讯作者:
Jong-Hong Park;Yeonghun Nam;Jong‐Moon Chung