Spectrum sensing via temporal convolutional network

In this paper, we investigate a spectrum-sensing system in the presence of a satellite, where the satellite works as a sensing node. Considering the conventional energy detection method is sensitive to the noise uncertainty, thus, a temporal convolutional network (TCN) based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing, relying on the offline training and the online detection stages. Specifically, in the offline training stage, spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose. Moreover, in the online detection stage, the well trained TCN is utilized to perform real-time spectrum sensing, which can upgrade spectrum-sensing performance by exploiting the temporal features. Additionally, simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection (ED), the convolutional neural network (CNN), and deep neural network (DNN). Furthermore, the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.