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An Improved Cooperative Spectrum Sensing Algorithm Based on Random Matrix Theory

机译:基于随机矩阵理论的改进的协作谱检测算法

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The combination of random matrix theory and cooperative spectrum sensing is one of the research hotspots of spectrum sensing. However, in multi-cognitive-user circumstance, the general combination algorithm has low detection performance under the case of few cognitive users. In this study, an improved CMME (Cooperation between the Maximum and Minimum Eigenvalue) based cooperative spectrum sensing algorithm was proposed. Through decomposing the signal achieve the goal of increase the number of logical sensed signals. The improved algorithm achieve the target of increasing signal related information. Moreover, the simulation results show that the proposed algorithm has a better perceived performance than classical CMME algorithm.
机译:随机矩阵理论和协作频谱感测的组合是光谱感测的研究热点之一。然而,在多认知用户环境中,在几个认知用户的情况下,一般组合算法的检测性能低。在该研究中,提出了一种改进的CMME(最大和最小特征值之间的协作)的协作频谱传感算法。通过分解信号实现增加逻辑感测信号的数量的目标。改进的算法实现了增加信号相关信息的目标。此外,仿真结果表明,该算法具有比经典CMME算法更好的感知性能。

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