Effective Spectrum Sensing By Weighted Data Fusion In Full Duplex Cognitive Radio Networks

As the full duplex (FD) technique becoming more sophisticated, a novel cognitive radio network (CRN) protocol called “ listen-and-talk ” (LAT) was proposed. Based on self-interference cancellation (SIC) technique, LAT protocol can promote the utilization of spectrum resources with the cost of the performance of spectrum sensing. Several researchers made efforts to overcome this shortcoming. In this paper, a more effective cooperative spectrum sensing strategy based on weighted data fusion was proposed. Convex optimization is used to solve the math model. Simulation results show that the method of weighted data fusion (WDF) highly improves the performance of spectrum sensing in FDCRN, also outperforms the previous studies.