Smart Sensing Strategy for SPRT Considering Fast Changing Sample Statistics in Cognitive Radio Systems

Abstract

This paper proposes an efficient design strategy for sequential detectors to reliably detect primary user's signals, especially in a fast fading scenario. We study computation of the log-likelihood ratio for coping with fast changing sample variances of received signal and noise, which are considered random variables. First, we analyze the detectability of the conventional generalized log-likelihood ratio (GLLR) scheme considering fast changing sample statistics caused by fast fading effects. Second, we propose a methodology for performing the sequential probability ratio test in a robust and efficient manner with known probability density functions of the received signal and noise variances. Finally, our simulation result shows that in this fast fading environments the proposed sensing method is robust and reduces sensing time compared with the detection using purely the conventional GLLR scheme.