Adjustable Rendezvous in Multi-Radio Cognitive Radio Networks
Adjustable Rendezvous in Multi-Radio Cognitive Radio Networks
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DOI：
10.1109/glocom.2014.7417407
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发表时间：
2014-12
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通讯作者：
Lu Yu;Hai Liu;Y. Leung;Xiaowen Chu;Zhiyong Lin
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文献类型：
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作者：
Lu Yu;Hai Liu;Y. Leung;Xiaowen Chu;Zhiyong Lin
重定向是认知用户建立通信链路，实现数据通信和网络管理的基本操作。大多数现有的会合算法隐含地假设每个认知用户配备有一个无线电，即，一个无线收发器。随着无线收发器成本的下降，利用多个无线电显著提高交会性能在经济上变得可行。在本文中，我们提出了一种可调多无线电交会（AMRR）算法，利用多个无线电的快速交会的基础上可用的信道。假设认知用户配备有m个无线电。我们的基本思想是将无线电划分为两组：k个驻留无线电和（m-k）个跳频无线电。用户停留在停留无线电中的特定信道上，同时在跳频无线电中的可用信道上跳频。我们证明了AMRR的最大交会时间（MTTR）的上界为O（|C_1|| C_2|/m_1m_2}），其中|C_1|和|C_2|是两个用户的可用信道的数量，并且m_1和m_2是两个用户的无线电设备的数量。当两个用户配备有相同数量的无线电时，该界限满足任何确定性会合算法的MTTR的下限（即，m_1=m_2）。AMRR是可调的，在MTTR或E（TTR）上通过调整k值来提供其最佳性能。仿真结果表明，AMRR性能优于国家的最先进的。
Rendezvous is a fundamental operation for cognitive users to establish communication links so as to realize data communications and network management. Most of existing rendezvous algorithms implicitly assume that each cognitive user is equipped with one radio, i.e., one wireless transceiver. As the cost of wireless transceivers is dropping, it becomes economically feasible to utilize multiple radios to significantly improve the rendezvous performance. In this paper, we propose an Adjustable Multi-Radio Rendezvous (AMRR) algorithm which exploits multiple radios for fast rendezvous based on available channels only. Suppose that a cognitive user is equipped with m radios. Our basic idea is to partition the radios into two groups: k stay radios and (m-k) hopping radios. The user stays on specific channels in the stay radios while hops on its available channels parallelly in the hopping radios. We prove that the maximum time-to-rendezvous (MTTR) of AMRR is upper-bounded by O(|C_1||C_2|/m_1m_2}), where |C_1| and |C_2| are the numbers of available channels of two users and m_1 and m_2 are the numbers of radios of the two users. This bound meets the lower bound of MTTR of any deterministic rendezvous algorithm when two users are equipped with the same number of radios (i.e., m_1=m_2). AMRR is adjustable in giving its best performance on either MTTR or E(TTR) by adjusting value of k. Simulation results show that AMRR performs better than the state-of-the-art.