Throughput enhancement of cognitive radio networks through imperfect spectrum predictions

Spectrum sensing is used to detect spectrum holes and find active primary users, while randomly selecting channel for sensing leads to secondary user´s lower throughput in high traffic cognitive radio networks. Spectrum prediction forecasts future channel states on the basis of historical information. A new frame structure is proposed in this paper for the imperfect spectrum prediction, improving SU´s throughput through selecting sensing channels from the channels predicted to be idle. Simulation results show that secondary user´s throughput is significantly enhanced by the imperfect spectrum prediction. The effects of traffic intensity, probability of wrong prediction, and channel number on the throughput are also investigated in this study.