Throughput oriented lightweight near-optimal rendezvous algorithm for cognitive radio networks
Throughput oriented lightweight near-optimal rendezvous algorithm for cognitive radio networks
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DOI：
10.1016/j.comnet.2018.03.009
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
C. Xin;Sharif Ullah;Min Song;Zhao Wu;Qiong Gu;Huanqing Cui
中科院分区：
文献类型：
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作者：
C. Xin;Sharif Ullah;Min Song;Zhao Wu;Qiong Gu;Huanqing Cui
在认知无线网络中，辅助用户必须动态搜索和访问主用户未使用的频谱。由于这种动态频谱接入的特性，二次用户之间的交会对认知无线网络来说是一个巨大的挑战。在本文中，我们提出了一种面向吞吐量的轻量级近最优交会（TENOR）算法，该算法不需要公共控制通道。TENOR具有非常轻的开销，并且在吞吐量和会合时间方面实现了近乎最佳的性能。使用TENOR，辅助用户被分组为节点对，这些节点对以分散的方式分布在不同的通道上。同信道干扰最小，吞吐量接近最佳。我们建立了一个数学模型来分析TENOR的性能。分析和仿真结果都表明，TENOR实现了接近最优的吞吐量和交会时间，并且显著优于文献中最先进的交会算法。
In cognitive radio networks, secondary users have to dynamically search and access spectrum unused by primary users. Due to this dynamic spectrum access nature, the rendezvous between secondary users is a great challenge for cognitive radio networks. In this paper, we propose aThroughput oriEnted lightweight Near-Optimal Rendezvous(TENOR) algorithm that does not need a common control channel. TENOR has very lightweight overhead and accomplishes near-optimal performance with regard to both throughput and rendezvous time. With TENOR, secondary users are grouped into node pairs that are spread onto different channels in a decentralized manner. The co-channel interference is minimized and the throughput is near optimal. We develop a mathematical model to analyze the performance of TENOR. Both analytical and simulation results indicate that TENOR achieves near-optimal throughput and rendezvous time, and significantly outperforms the state-of-the-art rendezvous algorithms in the literature.