Abstract
Aiming at problems that the eigenvalue based spectrum sensing algorithms don’t perform well in the situation of low SNR, small sample and need high computational complexity with eigenvalue decomposition, based on the difference value between maximum and minimum eigenvalue spectrum sensing algorithm (DMM), a difference value between the arithmetic mean and geometric mean eigenvalue spectrum sensing algorithm (DAG) with low computational complexity and dynamic threshold was proposed, which via the power method. Simulation results show that the DAG can improve performance over the classical algorithms in situation of low SNR, small samples and increased second users without reduction of computational complexity.
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Acknowledgment
This work was supported in part by:
(1) The National Science Foundation of China (61701521).
(2) The Certificate of China Postdoctoral Science Foundation Grant (2016M603044).
(3) The National Science Foundation of ShannXi (2018JQ6074).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gao, W., Jiang, F., Ma, F., Liu, W. (2019). A Novel DAG Spectrum Sensing Algorithm with Reducing Computational Complexity. In: Li, B., Yang, M., Yuan, H., Yan, Z. (eds) IoT as a Service. IoTaaS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-14657-3_46
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DOI: https://doi.org/10.1007/978-3-030-14657-3_46
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