Spectrum Sensing for DTMB System: A CNN Approach

Abstract

Spectrum sensing for the Digital Television Terrestrial Multimedia Broadcasting (DTMB) system is considered in this paper. A novel method, which contains a preprocessing method of partition and averaging, a convolutional neural network (CNN) and a threshold chosen scheme, is proposed to solve this problem. In the spectrum sensing process, the received signal is firstly preprocessed by a scheme of partition and averaging. Then it is fed into the well-trained CNN. The posterior probability of the signal being noise is output. Based on the Neyman-Pearson theorem, a threshold can be chosen by the Monte Carlo method. The result of spectrum sensing can be obtained by comparing the posterior probability with the threshold and the complexity of the proposed method is also analyzed. Through computer simulations, the advantage of the proposed method is demonstrated. At a low signal-to-noise ratio (SNR), the proposed method can achieve a satisfactory detection probability. Besides, the proposed method is robust to different SNRs and has good generalization performance under different noise assumptions. When the proposed method is transferred to sense DTMB signal under time dispersive channels, the performance does not deteriorate and even can be better, which shows the robustness of the proposed method to the time dispersive channels.