The modulation format is a key parameter that. influences the monitoring of the intercepted signals. Automatic modulation classification (AMC) is utilized to recognize the modulation format of the intercepted signals....
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ISBN:
(纸本)9781728186955
The modulation format is a key parameter that. influences the monitoring of the intercepted signals. Automatic modulation classification (AMC) is utilized to recognize the modulation format of the intercepted signals. However, most recent AMC methods neglect the complementarity accross different features. In this paper, we propose a novel feature fusion based AMC scheme using the convolutional neural network (FFCNN). Fused feature is generated by concatenating the two-dimensional spectrum correlation function (SCF) images and the graphic constellation (GC) images. Moreover, the FFCNN classifier is adopted to obtain more discriminative representations, leading to improved final modulation classification performance. Extensive simulations demonstrate that the proposed FFCNN scheme outperforms other recent methods.
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 pr...
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ISBN:
(纸本)9781509040995
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.
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