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Compressive wideband spectrum sensing in CRNs under primary user emulation attack

机译:主要用户仿真攻击下CRN中的压缩宽带频谱感知

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Spectrum sensing in a wideband regime for cognitive radio network (CRN) faces considerably technical challenge due to the constraints on ADCs, especially in the presence of primary user emulation attack (PUEA) where some malicious users try to impersonate the primary user (PU) to prevent other secondary users (SUs) from accessing the idle spectrum. To address this issue, an eigenvalue-based compressive wideband spectrum sensing (ECWSS) scheme is proposed in this paper. ECWSS directly utilizes the compressive measurements based on compressive sampling (CS) theory to perform wideband spectrum sensing without requiring signal recovery. In ECWSS, the sensors around a SU carry out compressive sampling at the subNyquist rate instead of the SU. Furthermore, the exact probability density function of extreme eigenvalues is used to set the threshold. Theoretical analysis and simulation results show that compared to the traditional sensing scheme, ECWSS has better detection performance and is more robust in the existence of PUEA.
机译:由于ADC的限制,认知无线电网络(CRN)的宽带方案中的频谱感知面临相当大的技术挑战,尤其是在存在主要用户仿真攻击(PUEA)的情况下,某些恶意用户试图模仿主要用户(PU)防止其他辅助用户(SU)访问空闲频谱。为了解决这个问题,本文提出了一种基于特征值的压缩宽带频谱感知(ECWSS)方案。 ECWSS直接利用基于压缩采样(CS)理论的压缩测量来执行宽带频谱感测,而无需信号恢复。在ECWSS中,SU周围的传感器以次奈奎斯特速率而不是SU进行压缩采样。此外,极限特征值的精确概率密度函数用于设置阈值。理论分析和仿真结果表明,与传统的感知方案相比,ECWSS具有更好的检测性能,并且在PUEA的存在下具有更强的鲁棒性。

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