Efficient Detection Using Sequential Probability Ratio Test in Mobile Cognitive Radio Systems
This paper proposes a smart design strategy for a sequential detector to reliably detect the primary user's signal, especially in fast fading environments. The authors study the computation of the log-likelihood ratio for coping with a fast changing received signal and noise sample variances, which are considered random variables. They analyze the detect-ability of the conventional Generalized Log-Likelihood Ratio (GLLR) scheme when considering fast changing statistics of unknown parameters caused by fast fading effects. They propose an efficient sensing algorithm for performing the sequential probability ratio test in a robust and efficient manner when the channel statistics are unknown. The proposed scheme is compared to the conventional method with simulation results with respect to the average number of samples required to reach a detection decision.