An improved energy detection scheme for cognitive radio networks in low SNR region

Abstract

Cognitive radio is considered as a new paradigm of designing wireless communication systems that aims at improving the spectrum utilization. Spectrum sensing is one of the most important elements in cognitive radio networks. Many spectrum sensing algorithms have been proposed in the literature. Among these algorithms, energy detection has been widely used in practice because of its computational and implementation simplicities. However, the performance of conventional energy detection is greatly deteriorating due to the impact of noise uncertainty, especially in low signal to noise ratio SNR environments. On the other hand, the inappropriate setting of the detection threshold could leads to a significant decline in the performance of detection. In this paper, we explore these situations and we propose an energy detection based optimal threshold scheme. We derived an optimal threshold level based on the tradeoff between misdetection probability and false alarm probability. We also developed an adaptive threshold factor with optimal algorithm in order to combat the noise uncertainty which is compatible with the real world communications and that allows gaining better spectrum sensing performance especially in low SNR.