Spectral minima tracking for improving the energy detection under noise power uncertainty

Abstract

Spectrum sensing involves identifying the unused license bands in Cognitive Radio Networks. Energy Detection (ED) is the most widely studied form of spectrum sensing. ED detects potentially unknown signals and it is mainly characterized by its low complexity. However, the ED performance is considerably deteriorated by the presence of noise uncertainty (NU). To increase the robustness of ED in NU scenarios, in this work we propose to use an adaptive detection threshold, which is calculated from the noise power estimate obtained at each sensing interval. The noise power is estimated using the technique called Spectral Minima Tracking (SMT). Moreover, a correlation analysis is conducted to quantify the degree of relationship between the parameters of the SMT technique and the resulting noise power estimate. The findings made in the correlation analysis allowed to adjust the parameters of the SMT technique, and thus giving place to the improvement of the quality of the noise power estimate. Simulation results show an improvement in the detection rate of the adaptive ED due to the reduction of the bias in the noise power estimate, as compared to the non-adaptive ED.