Fuzzy likelihood ratio test for cooperative spectrum sensing in cognitive radio

Abstract

Efficient and reliable spectrum sensing is an essential requirement in cognitive radio networks. One challenge faced in the spectrum sensing is the existence of the noise power uncertainty. This paper proposes a cooperative spectrum sensing scheme using fuzzy set theory to mitigate the noise power uncertainty. In this scheme, the noise power uncertainty in each Secondary User (SU) is modeled as a Fuzzy Hypothesis Test (FHT). We deploy the likelihood ratio test on the FHT to derive a fuzzy energy detector with a threshold that depends on the noise power uncertainty bound. The fusion center combines the received local hard decisions from the SUs and makes a final decision to detect the absence/presence of a Primary User (PU). We compare the performance of the proposed algorithm with some classical threshold-based energy detection schemes using receiver operating characteristic and detection probability versus the signal to noise ratio curves via Monte Carlo simulations. The proposed algorithm outperforms the cooperative spectrum sensing with a bi-thresholds energy detector and a simple energy detector.