Throughput performance improvement in cognitive radio networks based on spectrum prediction
Fukang Hou, Xin Chen, Hai Huang, Xiaojun Jing · 2016
Spectrum sensing is used to detect spectrum holes and find active primary users while randomly selecting channel for sensing leads to secondary user's low throughput in cognitive radio networks. Spectrum prediction forecasts future channel status on the basis of historical information, and BP neural network is used as an example of the prediction method. Second users' frame structure is redesigned in this paper based on spectrum prediction, aiming to select the most likely idle channel for sensing. Theoretical and simulation results show that secondary users' throughputs are significantly improved by spectrum prediction. Correlations both in the frequency domain and time domain are compared, and the impacts of the prediction method, traffic intensity, and channel number on the throughput are also investigated in this study.