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Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network

机译:基于多电源基于Rendezvous技术的异构认知无线电传感器网络

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摘要

In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for distributed CR is inefficient in a dynamic network. As a result, non-assisted blind rendezvous, which is unaware of its counterpart node, has recently led to a lot of interest in the research arena. In this paper, we study a channel rendezvous method based on prime number theory and propose a new multi-radio-based technique for non-assisted rendezvous with the blind and heterogeneous condition. The required time and the optimal number of radios for the guaranteed rendezvous are calculated using probability-based measurement. Analytical expressions for probabilistic guaranteed rendezvous conditions are derived and verified by Monte Carlo simulation. In addition, the maximum time to rendezvous (MTTR) is derived in closed form using statistical and probabilistic analysis. Under different channel conditions, our proposed solution leads to a substantial time reduction for guaranteed rendezvous. For the sake of over-performance of our proposed system, the simulation outcome is compared to a recently proposed heterogeneous and blind rendezvous method. The Matlab simulation results show that our proposed system’s MTTR gains range from 11% to over 95% for various parametric values of the system model.
机译:在分布式认知无线电(CR)传感器网络中,空置通道上的传输和接收需要认知无线电节点来实现Rendezvous。由于缺乏来自网络环境的足够辅助,例如中央控制器和其他节点,因此在动态网络中辅助Rendezvous用于分布式CR的效率低。因此,不辅助盲区与对手节点不知道的盲目的会法,最近导致了对研究竞技场的很多兴趣。本文研究了基于素数理论的信道集合法,提出了一种新的基于多无线电基的非辅助与异构条件。使用基于概率的测量计算了保证的Rendezvous的所需时间和最佳无线电数。通过Monte Carlo模拟导出和验证了概率保证的Rendezvous条件的分析表达。此外,使用统计和概率分析,以封闭形式导出的最长时间(MTTR)。在不同的渠道条件下,我们所提出的解决方案导致保证的约会的大幅减少。为了过度表现,我们提出的系统,将模拟结果与最近提出的异构和盲的Rendezvous方法进行比较。 MATLAB仿真结果表明,对于系统模型的各种参数值,我们所提出的系统的MTTR增益的增长率为11%至超过95%。

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