Spectrum Sensing Method Based on Residual Cellular Network

The traditional spectrum sensing method based on convolutional neural network (CNN) has the single-branch convolutional network structure and the shallow network structure which limits the ability of extracting the Primary User (PU) feature. Aiming at these problems, a spectrum sensing method based on the residual cellular network (ResCelNet) is proposed in this work, involving a structure of “dual-branch convolution plus summation operation plus residual learning plus dual-branch convolution plus summation operation”. Specifically, the dual-branch convolution improves the feature extraction ability, the addition operation enhances the micro-feature information, and the residual learning is adopted to facilitate training the deep spectrum sensing network. This method transforms the spectrum sensing problem into the image binary classification problem. Firstly, the received signals are reshaped into a matrix and normalized to gray levels, which is used as the input of the network. Secondly, the feature information of gray-scale images is extracted and the network is trained through dual-branch convolution and residual learning. Finally, the test data is input into the trained model and spectrum sensing based on image classification is implemented. The experimental results demonstrate that the proposed method exhibits a higher detection probability and a lower false alarm probability as well as better generalization ability as compared with the traditional methods under low signal to noise ratio (SNR) circumstance.