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A Spectrum Handoff Scheme for Optimal Network Selection in NEMO based Cognitive Radio Vehicular Networks: A Cost Function MADM Method based on Grey Theory based Approach

机译:基于Nemo认知无线电车辆网络的最优网络选择的频谱切换方案:基于灰色理论方法的成本函数MADM方法

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Multiple Attribute Decision Making (MADM) is one of the intelligent network selection methodologies for Intelligent Transportation System (ITS). The fundamental objective of MADM method is to select the optimal network among the finite set of alternative. In this paper, we have proposed a cost function MADM method based on the grey theory based approach which outperforms the existing MADM method for ITS. The method is validated to perform a spectrum handoff for optimal network selection in Network Mobility (NEMO) based Cognitive Radio (CR) vehicular networks. In NEMO based CR vehicular networks, the Mobile Reuter (MR) requires spectrum handoff when a mobile network changes its point of attachment in CR vehicular networks. It becomes difficult for MR to select the optimal network from the available access networks. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results revel that the proposed MADM method is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.
机译:多属性决策(MADM)是智能交通系统的智能网络选择方法(ITS)中的一个。 MADM方法的基本目标是选择有限集合替代之中的最佳网络。在本文中,我们提出了基于灰色理论为基础的方法,其优于其现有的多属性决策方法的成本函数MADM方法。该方法被验证以执行基于在网络移动性(NEMO)最佳网络选择认知无线电(CR)车载网络的频谱的越区切换。在基于CR NEMO车载网络,移动路透(MR)要求越区切换的频谱时的移动网络改变其连接点在CR车载网络。就很难对MR选择从可用接入网络的最佳网络。的MADM方法本申请提供了具有服务质量的可用网络之间更宽的和最佳的选择。数值结果陶醉,所提出的MADM方法是有效的,用于具有降低的基于NEMO CR车载网络的复杂最佳网络选择光谱越区切换决策。

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