… in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine
learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper …

… Abstract—We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive
radio (CR) networks based on machine learning techniques which are used for pattern classifi…

… spectrum sensing time. We derive the performance bounds by translating the Bayes error rate
into spectrum sensing error … by simulation to achieve higher spectrum sensing accuracy, in …

… The application of Machine Learning techniques for Spectrum Sensing has a drawn interest
in … on Machine Learning techniques for Cooperative Spectrum Sensing. We compare three …

… As aforementioned, machine learning could … spectrum sensing by an end-to-end machine
learning 140 algorithm. The relationship between the sub-Nyquist samples 141 and spectrum …

… To evaluate the performance of the proposed algorithm, we have compared the proposed
approach with other machine learning-based cooperative spectrum sensing, including K-outof-…

… Thus, the reliability of spectrum sensing is still an open issue in … a spectrum sensing
method based on a machine learning theory for cognitive radio networks. The spectrum sensing …

… In this study, the authors consider the application of machine learning (ML) models in
cooperative spectrum sensing of cognitive radio networks (CRNs). Based on a statistical analysis …

… Also, we try to grasp the effect of the fading channels on the sensing … spectrum sensing
techniques. Finally, we investigate the circumstances in which KMeans-based spectrum sensing …

… cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning …
A low-dimensional probability vector is proposed as the feature vector for machine learning …