Abstract
Owing to no need for prior knowledge of signal, blind spectrum sensing has received wide attention. Covariance Absolute Value (CAV) detection algorithm, one of the most popular blind sensing algorithms, considers the correlation of signal samples. However, its detection performance is restricted by the uncertain threshold calculation. To optimize the performance of CAV, we propose a new method based on a new statistic and goodness of fit test. The statistic is constructed from the off-diagonal of covariance matrix firstly, then Anderson-Darling (AD) test is used to estimate the existence or absence of primary user. The proposed method not only achieves blind detection but also improves the sensing performance of CAV. Experimental results manifest the effectiveness of the proposed scheme.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (Project 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.
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Chen, Y., Jing, X., Mu, J., Li, J. (2019). An Improved Covariance Spectrum Sensing Algorithm Establish on AD Test. In: Sun, S. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2018. Lecture Notes in Electrical Engineering, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-13-1733-0_3
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DOI: https://doi.org/10.1007/978-981-13-1733-0_3
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