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- W2556143762 abstract "Spectrum sensing is a key component in cognitive radio networks, which allows secondary users to communicate without causing harmful interference to primary users. Cyclostationary feature based spectrum sensing has proven preferable to other methods under low signal-to-noise ratio conditions. To detect the presence of primary signals, conventional cyclostationary feature based schemes tend to simply compare the values of signal features to a predefined threshold. However, such schemes would lead to dramatic performance degradation when signal features are overwhelmed by noise. This paper proposes a novel scheme that applies the low-rank and sparse decomposition technique to cyclostationary feature based spectrum sensing. The spectrum correlation function matrix is decomposed into two matrices, of which the low-rank one represents noise and interference while the sparse one represents cyclostationary features of PU signal. Subsequently, the scheme takes advantage of the signal features in the sparse matrix to determine the presence of PU signal. Simulation results have demonstrated the superiority of our proposed scheme in terms of detection probability and false alarm probability." @default.
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- W2556143762 date "2016-09-01" @default.
- W2556143762 modified "2023-10-17" @default.
- W2556143762 title "Cyclostationary feature based spectrum sensing via low-rank and sparse decomposition in cognitive radio networks" @default.
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- W2556143762 doi "https://doi.org/10.1109/iscit.2016.7751706" @default.
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