Journal
ENTROPY
Volume 24, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/e24010129
Keywords
cooperative spectrum sensing; cognitive radio network; deep learning; large dynamic signal-to-noise ratio
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Funding
- General Program of National Natural Science Foundation of China [61871157, 62071143]
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This paper introduces a cooperative spectrum sensing scheme based on a CNN-GRU network, which enhances sensing reliability by fusing local information from different nodes. Experimental results show that the proposed method achieves competitive performance under the condition of large dynamic signal-to-noise ratio.
Cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.
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