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Overhead Analysis for Radio Environment Mapenabled Cognitive Radio Networks

机译:启用无线电环境映射的认知无线电网络的开销分析

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This paper presents a novel and general approach to cognitive radio (CR) networking based on the Radio Environment Map (REM). REM is envisioned to be an integrated database that consists of comprehensive multi-domain information for CR, such as geographical features, available services, spectral regulations, locations and activities of radio devices, policies, and past experiences. Disseminating and sharing REM information offers a proper vehicle of CR network support, which can be exploited by cognitive engine (CE) for most cognitive functionalities such as situation awareness, reasoning, learning, planning and decision support. Tradeoffs have to be made between the performance gain and the cost of overhead. This paper focuses on analyzing the overhead associated with REM dissemination under various scenarios. With analytical models and network simulations, it is shown that the overhead of REM dissemination can be significantly reduced by extending the optimized link state routing protocol (OLSR). Application-specific ad hoc methods have also been proposed and can be employed to further reduce the overhead. Simulations are presented, comparing the overhead of REM dissemination for different network size, topology, node density and mobility. Preliminary results show that the speed of wireless nodes has little impact to the load of overhead if the REM dissemination rate is fixed. The size of REM information element is estimated for the emerging cognitive wireless regional area networks (IEEE 802.22 WRAN).
机译:本文提出了一种基于无线电环境图(REM)的新颖通用的认知无线电(CR)网络方法。 REM被设想为一个集成的数据库,其中包含有关CR的全面的多域信息,例如地理特征,可用服务,频谱法规,无线电设备的位置和活动,政策以及过去的经验。传播和共享REM信息可以为CR网络提供适当的支持,认知引擎(CE)可以利用它来实现大多数认知功能,例如情况感知,推理,学习,计划和决策支持。必须在性能提升和开销成本之间进行权衡。本文着重分析在各种情况下与REM传播相关的开销。通过分析模型和网络仿真,可以证明,通过扩展优化的链路状态路由协议(OLSR),可以大大减少REM分发的开销。还已经提出了专用的临时方法,可以将其用于进一步减少开销。进行了仿真,比较了针对不同网络规模,拓扑,节点密度和移动性的REM传播开销。初步结果表明,如果REM传播速率固定,则无线节点的速度对开销的负荷影响很小。为新兴的认知无线局域网(IEEE 802.22 WRAN)估计REM信息元素的大小。

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