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Sensing Time Optimisation and Sensor Selection in Multi-channel Multi-antenna Wireless Cognitive Sensor Networks

机译:多通道多天线无线认知传感器网络中的传感时间优化与传感器选择

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Energy efficiency of cooperative spectrum sensing is a critical issue in wireless cognitive sensor networks (CSNs). However, sensor nodes consume high energy to support cognitive radio (CR) functionalities such as channel sensing. In this study, the energy-efficient cooperative spectrum sensing in multi-channel multi-antenna CSNs is investigated. First, a multi-channel CSN is considered to obtain sensing sensors and sensing time for each band in a CR system. The criteria is to minimise energy consumption of cooperative spectrum sensing under the global probabilities of detection and false alarm constraints. The authors employ a convex optimisation method to obtain optimal sensing time and sensing nodes for each band. Next, their analysis is extended into multi-antenna sensors, which is a promising solution instead of using more sensors. In multi-antenna cooperative spectrum sensing, diversity helps sensors behave virtually the same as multi-sensors systems. Then, the problem of sensor selection and also setting the sensing time for each band is solved based on the convex optimisation framework. Simulation results highlight that in their proposed algorithms a significant energy is saved compared with the baseline methods with equal sensing time in all bands.
机译:合作频谱感测的能效是无线认知传感器网络(CSN)中的关键问题。但是,传感器节点会消耗大量能量来支持认知无线电(CR)功能,例如通道感测。在这项研究中,研究了多通道多天线CSN中的节能协作频谱感测。首先,考虑使用多通道CSN获得CR系统中每个频段的感应传感器和感应时间。标准是在检测和错误警报约束的全球可能性下,使协作频谱感测的能耗最小化。作者采用凸优化方法来获得每个频段的最佳传感时间和传感节点。接下来,他们的分析扩展到多天线传感器,这是一个有前途的解决方案,而不是使用更多的传感器。在多天线协作频谱感测中,分集可帮助传感器的行为与多传感器系统几乎相同。然后,基于凸优化框架,解决了传感器选择以及每个波段设置感应时间的问题。仿真结果表明,与基线方法相比,在他们提出的算法中,在所有频段上均具有相等的传感时间,可节省大量能源。

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