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Cooperative spectrum sensing based on a low-complexity cyclostationary detection method for cognitive radio networks

机译:基于低复杂度循环平稳检测方法的认知无线电网络协作频谱感知

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Fast reliable spectrum sensing (SS) is a crucial problem in the cognitive radio systems. To address this issue, cyclostationarity-based detection methods, which are generally more complex but more reliable than energy detection methods, have been proposed. This paper presents a new method to detect the presence of the second-order cyclostationarity in the OFDM-based primary user (PU) signals. The proposed method has a low computational complexity, while it presents a close performance as compared to the well-known GLRT-based Dandawaté-Giannakis's method. Moreover, since the proposed low-complexity method is robust against the noise uncertainty, it can be a good alternative to the energy detection method. In order to be able to perform the hypothesis test, we derive the asymptotic distribution of the test statistic under the null hypothesis. We further propose some cooperative spectrum sensing methods to improve the detection performance. An approximate threshold selection method is proposed for performing statistical test at the fusion center (FC). Extensive simulation results confirms the superiority of the proposed schemes.
机译:快速可靠的频谱感测(SS)是认知无线电系统中的关键问题。为了解决这个问题,已经提出了基于循环平稳性的检测方法,该方法通常比能量检测方法更复杂但更可靠。本文提出了一种新的方法来检测基于OFDM的主要用户(PU)信号中二阶循环平稳性的存在。与众所周知的基于GLRT的Dandawaté-Giannakis方法相比,所提出的方法具有较低的计算复杂度,同时具有接近的性能。而且,由于所提出的低复杂度方法对于噪声不确定性具有鲁棒性,因此它可以替代能量检测方法。为了能够执行假设检验,我们在零假设下得出了检验统计量的渐近分布。我们进一步提出了一些协作频谱感知方法来提高检测性能。提出了一种近似阈值选择方法,用于在融合中心(FC)进行统计测试。大量的仿真结果证实了所提方案的优越性。

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