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Fusion Test Statistics Based Mixture Detector for Spectrum Sensing
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AI
DOI：10.1109/TVT.2021.3139126.png)
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摘要
En 中文
In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms.
Keyword:
Detectors
Sensors
Covariance matrices
Random variables
Approximation algorithms
Eigenvalues and eigenfunctions
Complexity theory
Cognitive radio
spectrum sensing
fusion test statistics
mixture detector
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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