Improved Energy Detector with Weights for Primary User Status Changes in Cognitive Radio Networks

Abstract

This paper presents an improved energy detector (ED) with weights to improve detection performance in this situation that the primary user's status changes as arriving or leaving randomly in cognitive radio networks. The idea is derived from the concept of unequal scale sampling such that the instantaneous energy statistic of the sampling points in the sensing period is endowed with monotonic weights. The probabilities of false alarm and detection for our ED are deduced under the new detection model. Numerical simulation results show that the proposed ED offers better detection performances with a reduced probability of false alarm compared to conventional ED, and improved energy detector can improve the overall probabilities of detection, when spectrum sensing is performed for a network with high traffic.