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Cognitive cross-layer multipath probabilistic routing for cognitive networks

机译:认知网络的认知跨层多路径概率路由

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Mobile Ad-hoc NETworks (MANETs) is a set of mobile nodes that can move around arbitrarily, and communicate with others in a multi-hop fashion without any assistance of base stations. With recent advances in Cognitive Radio (CR) technology, it is possible to apply the Dynamic Spectrum Access model in MANETs. This introduces the concept of Cognitive Radio Ad Hoc Networks (CRAHNs). Applying CR techniques provides better throughput, even in congested spectrum along with better propagation characteristics. CRAHN is a kind of intelligent network that is aware of its surrounding environment, and adapts to the transmission or reception parameters to achieve efficient communication without interfering with primary users. Routing in CR environment is a challenging task as the availability of channel is constrained by the presence of primary user. The problem of routing in CRAHNs targets the creation and maintenance of wireless multi-hop paths among cognitive nodes by deciding both the spectrum to be used and the relay nodes of the path. This paper proposes a cognitive cross-layer multipath probabilistic routing for cognitive radio based networks. The proposed solution uses spectrum holes identified by MAC layer, decides the channel to be used and transmit power level for each hop in the path. The proposed solution is implemented in NS2, and performance of the proposed solution is compared with the existing solution from the literature. The paper also shows that the proposed solution outperforms existing solution in terms of packet delivery ratio, average end-to-end delay and energy consumed per data packet.
机译:移动Ad-hoc NETworks(MANET)是一组移动节点,可以任意移动,并在没有基站任何帮助的情况下以多跳方式与其他节点通信。随着认知无线电(CR)技术的最新发展,可以在MANET中应用动态频谱访问模型。这介绍了认知无线电自组织网络(CRAHN)的概念。应用CR技术可提供更好的吞吐量,即使在拥挤的频谱中也具有更好的传播特性。 CRAHN是一种智能网络,它了解其周围的环境,并适应传输或接收参数以实现有效的通信而不会干扰主要用户。由于主要用户的存在限制了信道的可用性,因此在CR环境中进行路由是一项具有挑战性的任务。 CRAHN中的路由问题旨在通过确定要使用的频谱和路径的中继节点来在认知节点之间创建和维护无线多跳路径。本文提出了一种基于认知无线电的网络的认知跨层多径概率路由。所提出的解决方案使用由MAC层标识的频谱空洞,确定要使用的信道并为路径中的每个跳发送功率电平。所提出的解决方案在NS2中实现,并将所提出的解决方案的性能与文献中的现有解决方案进行了比较。本文还表明,在数据包传输率,平均端到端延迟和每个数据包的能耗方面,该解决方案优于现有解决方案。

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