Percolation Degree of Secondary Users in Cognitive Networks
Percolation Degree of Secondary Users in Cognitive Networks
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DOI：
10.1109/jsac.2012.121114
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发表时间：
2012-10
影响因子：
16.4
通讯作者：
Luoyi Fu;Liang Qian;Xiaohua Tian;Huan Tang;Ning Liu;Guanglin Zhang;Xinbing Wang
中科院分区：
文献类型：
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作者：
Luoyi Fu;Liang Qian;Xiaohua Tian;Huan Tang;Ning Liu;Guanglin Zhang;Xinbing Wang
认知网络指的是两个重叠的结构，称为主要网络和次要网络共存。主网由被授权的频谱用户的主节点组成，而辅助网络由必须机会性地接入被授权的频谱的未授权用户组成。在本文中，我们研究了在大规模认知无线电网络中实现k-渗流的二级网络的渗漏度。渗漏度定义为当渗滤簇中任意两个次级中继之间至少存在k条顶点不相交路径时，每个次级用户的最近邻居的数目。当存在跨越整个网络的无限数量的相互连接的次级用户时，形成渗滤簇。集群中的每个用户可能连接到多个邻居，从而在他们中的任何两个之间建立更多通信链路。由于位于边界附近的节点邻居较少，边界效应成为决定渗流程度的瓶颈。对于认知网络，当主节点密度变得相当大时，边界效应在网络内部扩散。由于主网的限制，位于主节点附近的大多数次用户的传输面积减小。因此，为了确保渗透簇中的k-连通性，每个辅助用户必须连接到更多的邻居，并且辅助网络的渗透度产生主节点密度的函数。关于认知网络的拓扑变化，我们将这种关系指定为三个区域。给出了不同一次节点密度下的渗流程度的闭合表达式。该表达式表征了二次渗流簇中的连通性强度，从而为认知网络中容错能力的提高提供了分析见解。
A cognitive network refers to the one where two overlaid structures, called primary and secondary networks coexist. The primary network consists of primary nodes who are licensed spectrum users while the secondary network comprises unauthorized users that have to access the licensed spectrum opportunistically. In this paper, we study the percolation degree of the secondary network to achieve k-percolation in large scale cognitive radio networks. The percolation degree is defined as the number of nearest neighbors for each secondary user when there are at least k vertex-disjoint paths existing between any two secondary relays in the percolated cluster. The percolated cluster is formed when there are an infinite number of mutually connected secondary users spanning the whole network. Each user in the cluster is possibly connected to several neighbors, inducing more communication links between any two of them. Since nodes located near the boundary have fewer neighbors, the boundary effect becomes a bottleneck in determining the percolation degree. For cognitive networks, when the primary node density becomes considerably large, the boundary effect spreads inside the network. The transmission area of most secondary users who are located near the primary nodes decreases due to the restriction of the primary network. Therefore, to ensure k-connectivity in the percolated cluster, each secondary user must be connected to more neighbors, and the percolation degree of the secondary network yields a function of the primary node density. We specify the relationship into three regimes regarding the topology variation of the cognitive network. A closed-form expression of the percolation degree under different primary node densities is presented. The expression characterizes the connectivity strength in the secondary percolated cluster, therefore providing analytical insight on fault tolerance improvement in cognitive networks.