Optimal Design of Sequential Detector Considering Fast Fading in Cognitive Radio Networks

Abstract

In this paper, we studied the cooperative sensing via sequential detection in cognitive radio networks. Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision. In the scheme, each cognitive radio computes the log-likelihood ratio for its every measurement, and the base station sequentially accumulates these log-likelihood statistics and determines whether to stop making measurement. So far, most papers related to spectrum sensing in cognitive radio networks using sequential detection have ignored fading effect or assumed slow fading environments. These assumptions are inevitable to consider acquired raw samples by detector as independent and identically distributed samples during the sensing duration. The paper studies how to design the sequential detector in a robust manner in fast fading environments. In this case, the acquired samples are independent and not identically distributed. We propose an optimal independent and identical distribution (i.i.d.) approximation which has better performance than the conventional scheme using the generalized log-likelihood ratio (GLLR). One assumes both the signal and noise are gaussian distributed at each sample time.