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, the authors first analyze the interference impact on the energy detection in a Heterogeneous Cognitive Wireless Network (HCWN). Then, they 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.