认知无线电网络中基于仲裁的自适应跳频分布式协调方案
Adaptive Quorum-Based Channel-Hopping Distributed Coordination Scheme for Cognitive Radio Networks
摘要：
部署认知无线电网络（CRN）的最重要的挑战之一是找到一个公共控制信道（CCC）的所有次要用户（SU），使有效的CR通信。这种挑战归因于网络拓扑、位置和频谱可用性条件的动态时变变化。作为建立控制通信的过程的重定向是实现任何两对CR节点之间的有效通信的基本要求。最流行的CR会合协议是基于quorum系统（QS）。仲裁系统是系统的方法，它有几个有吸引力的属性，可以用来建立通信，而不需要一个CCC，从而克服了会合（RDV）问题。在这篇论文中，我们提出了一种新的基于跳频信道的分布式会合算法。该算法根据仲裁系统的交集特性，允许CR节点更频繁地相遇，从而提高了单个周期内RDV的概率。我们提出的算法被称为自适应_基于仲裁的跳频分布式协调方案的认知无线电网络。该算法的主要思想是根据CRN中业务负载的变化动态调整CR用户选择的QS。该算法减少了平均交会时间，提高了交会概率。我们通过Matlab仿真评估我们的算法的性能。对两种不同设计方案的性能进行了比较。实验结果表明，该算法可以降低TTR，提高RDV，降低每次成功RDV的能耗。
英文摘要：
One of the most important challenges in deploying cognitive radio networks (CRNs) is to find a common control channel (CCC) to all secondary users (SUs) that enables efficient CR communications. This challenge is attributed to the dynamic time-varying change of network topology, location and spectrum availability conditions. Rendezvous, which is the process of establishing control communications, is an essential requirement to enable efficient communication between any two pair of CR nodes. The most popular CR rendezvous protocols are based on quorum systems (QSs). Quorum systems are systematic approaches, which have several attractive properties that can be utilized to establish communication without the need of a CCC and so overcome the rendezvous (RDV) problem. In this thesis, we propose new channel-hopping-based distributed rendezvous algorithm based on grid-based-quorum techniques. The proposed algorithm increases the probability of RDV within a single cycle by allowing CR nodes to meet more often according to intersection property of quorum systems. Our proposed algorithm is called Adaptive_Quorum-Based Channel-Hopping Distributed Coordination Scheme for Cognitive Radio Networks. The main idea of our algorithm is to dynamically adjusting the selected QS by CR users according to the varying traffic loads in the CRN. The proposed algorithm decreases the average time to rendezvous (TTR) and increase the probability of RDV. We evaluate the performance of our algorithm through Matlab simulations. The performance of proposed algorithm is compared with two different design scheme. The results show that our algorithm can reduce TTR, increase the RDV, and decrease the energy consumption per successful RDV.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
