A fuzzy statistical test of fuzzy hypotheses
Fuzzy Sets and Systems
Testing fuzzy hypotheses with crisp data
Fuzzy Sets and Systems
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Entropy-based spectrum sensing in cognitive radio
Signal Processing
Multiple antenna spectrum sensing in cognitive radios
IEEE Transactions on Wireless Communications
Cooperative covariance and eigenvalue based detections for robust sensing
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Adaptive two thresholds based energy detection for cooperative spectrum sensing
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Cooperative spectrum sensing in cognitive radio networks: A survey
Physical Communication
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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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.