Higher-order statistics based sequential spectrum sensing for cognitive radio

Abstract

In cognitive radio (CR), spectrum sensing is a key enabling functionality to discover the vacant spectrum which is not occupied by primary systems. With good sensing capability, secondary users can effectively recycle the spectrum resource without disturbing active primary users. Energy detector (ED) is a commonly used and relatively simple spectrum sensing technique. In realistic environments, the CR receiver might operate at low signal-to-noise ratio (SNR) regimes due to the channel fading and noise uncertainty. At low SNR cases, the performance of the EDs degrades dramatically as the signal and noise are mixed together after the operation of energy calculation. In this paper, a high-order statistics (HOS) based sequential test detector is investigated to sense the underutilized spectrum, particularly for low-SNR applications. We resort to HOS, in terms of cumulant statistics, for overwhelming the Gaussian noise effect and improving the spectrum sensing reliability. Based on these cumulants, a binary hypothesis testing problem is formulated and a low-complexity sequential probability ratio test (SPRT) is developed for efficiently and fast detecting the vacant spectrum so as to meet the sensing duration requirements. Our numerical results show that the proposed detector outperforms the conventional EDs at extremely low SNR environments.