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Normalized Energy Detection Based Cooperative Spectrum Sensing with Reporting Errors in Heterogeneous Cognitive Radio Networks

机译:基于归一化能量检测的基于能量检测与异构认知无线电网络中报告误差的协同频谱感应

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In this paper, we investigated the cooperative sensing in a heterogeneous cognitive radio (CR) network scenario, where each secondary user (SU) may be equipped with different number of receive antennas and have different signal processing capacity, e.g., sampling rate. By considering the discrepancy in sensing reliability of different SUs, we extended the existing researches to propose an optimal cooperative sensing (OCS) scheme based on normalized energy detection (NED) by considering reporting errors, i.e., the fusion center (FC) may receive erroneous results from some SUs due to channel fading. It is derived that the proposed OCS scheme is the linear combination of modified local detection statistics. The optimal combining coefficient is just a function of the numbers of the antennas and the received signal samples, the signal to noise ratio (SNR) and the variance of the reporting errors at each SU. Meanwhile, the performance of the proposed OCS scheme, the well-known equal gain combination (EGC) method and the maximum normalized energy (MNE) detector with reporting errors is analytically derived, respectively. Furthermore, to avoid the prior information of each SU's SNR and the variance of the reporting errors, and simplify the decision-making as well as threshold setting, a sub-optimal cooperative spectrum sensing (SOCS) is proposed. Numerical results show that the proposed OCS and SOCS schemes all perform much better than the existing EGC and MNE methods.
机译:在本文中,我们研究了异构认知无线电(CR)网络场景中的协同感测,其中每个次级用户(SU)可以配备有不同数量的接收天线,并且具有不同的信号处理容量,例如采样率。通过考虑对不同SUS的可靠性的差异,我们通过考虑报告错误,即融合中心(FC)来扩展了基于归一化能量检测(NED)的最佳协作感测(OCS)方案的现有研究,即融合中心(FC)可能会接收错误由于通道衰落引起的一些SUS结果。推导出所提出的OCS方案是修改的局部检测统计的线性组合。最佳组合系数仅是天线的数量和接收信号样本的函数,信噪比(SNR)的信号和每个SU处的报告误差的方差。同时,分别分别进行了所提出的OCS方案的性能,众所周知的等增益组合(EGC)方法和具有报告误差的最大归一化能量(MNE)检测器。此外,为了避免每个SU的SNR的先前信息和报告误差的方差,并简化决策以及阈值设置,提出了次优的协作频谱感测(SOC)。数值结果表明,所提出的OCS和SOC方案都表现出比现有EGC和MNE方法更好。

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