A Hybrid Spectrum Prediction Model Based on Deep Learning
Jing Xia, Zheng Dou, Lin Qi, Guangzhen Si · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Spectrum prediction can further improve the performance of cognitive radio system, save spectrum sensing time and improve the communication quality of secondary users. To improve the accuracy of spectrum prediction and reduce the interference to the primiary users from the perspective of prediction, a hybrid spectrum prediction model based on deep learning is proposed in this paper. Specifically, the hybrid spectrum prediction model is composed of spectrum state regularity prediction (SSRP) model and enhanced available duration prediction (EADP) model, which respectively act on spectrum sensing and spectrum decision in cognitive radio. On the one hand, to improve the prediction precision of multi-channel joint spectrum state, SSRP model is proposed to analyze the regularity of joint spectrum state. SSRP model is used to extract frequency variation characteristics and spectrum occupancy characteristics by designing double networks, so as to excavate spectrum correlation in depth and achieve better prediction performance. On the other hand, to make full use of the joint spectrum state information without causing information redundancy, the EADP model of enhanced attention is proposed by integrating the attention mechanism with the spectrum sensing results. The simulation results show that SSRP model can obtain higher prediction accuracy and more stable distribution error to a certain extent, comparing with traditional spectrum state prediction methods. In addition, our innovatively proposed EADP model can reduce the available duration prediction error on the basis of integrating the attention mechanism.