Signal uncertainty in spectrum sensing for cognitive radio

摘要

The inability to perfectly know the system noise properties to infinite precision, referred to as noise uncertainty,\udresults in noise power calibration errors that have been proven to impose fundamental limitations on the detection performance\udof any spectrum sensing (signal detection) method in cognitive radio networks. In this work we argue that the inability of\udcognitive radio users to perfectly know beforehand the primary signals that might be present in the sensed band and their\udproperties, referred to as signal uncertainty in this work, also\udresults in an additional detection performance degradation.\udThe noise uncertainty consequences have widely been studied, verified experimentally and distilled into tractable mathematical\udmodels. However, the potential effects of the particular primary\udsignal properties on the resulting detection probability of generic\udspectrum sensing algorithms, such as energy detection, have not been taken into account in the analysis and performance\udevaluation of spectrum sensing in cognitive radio networks. In this context, this work develops a mathematical model for signal\uduncertainty and, based on such model, analyzes the impact of signal uncertainty on the resulting detection performance\udof spectrum sensing, with and without noise uncertainty, and compares the practical consequences of both degrading effects