Improved energy detection with interference cancellation in heterogeneous cognitive wireless networks

Energy detection is the most popular method among spectrum sensing techniques due to its low implementation complexity. However, its performance will be severely degraded by the interference from other secondary users (SUs). In this paper, we first analyze the interference impact on the energy detection in a heterogeneous cognitive wireless network (HCWN). Then, we propose an interference cancellation based energy detection method, referred to as ICED, to combat the interference. Based on ICED, closed-form expressions of the false alarm probability and detection probability are derived for the local detection and the results are extended to the cooperative detection as well. Our analysis is validated by numerical and simulation results which show that the proposed ICED method can significantly improve the performance of energy detection when there exists an interference SU in the HCWN. We also observe that ICED can achieve higher detection probability than the energy detection without interference under certain conditions.