… filter [16] based spectrum sensing schemes require … spectrum sensing in CRNs [18], [19],
[20], [21], [22]. However, recently, the efficacy of machine learning (ML) based spectrum sensing …

… Nyquist sampling for spectrum sensing has the … spectrum is still a challenging issue for this
approach. Along this line, this paper proposes an algorithm for narrowband spectrum sensing …

… a machine learning-based reliable spectrum sensing algorithm in … In the training phase, CR
users perform spectrum sensing, and … , the sensing report is assigned to a sensing class. The …

… CSS with NOMA to further boost spectrum efficiency. Since the … Therefore, we adopt the
machine learning-enabled solutions to … Furthermore, a variety of machine learning-enabled CSS …

… In responding to this paper, we propose a machine learning based fusion center algorithm
… spectrum sensing. The new fusion algorithm based on training a machine learning classifier …

… machine learning algorithm can be (re)trained to maintain high fidelity in machine learning
tasks… In particular, we apply SAGA to generate training data for the spectrum sensing problem. …

… CRN consists of battery-limited sensors. This article investigates the application of machine
learning technologies for cooperative spectrum sensing, especially through solving a multi-…

… learning-based efficient spectrum sensing method is proposed which follows a cooperative
spectrum sensing … The CR nodes build a machine learning-based classifier for detecting the …

… which are constructed based on machine learning concepts and principles. In addition,
this review aims to present a general classification of these machine learningbased schemes. …

… Machine learning (ML) algorithms are powerful techniques for designing a promising spectrum
sensing model. In this work, the supervised ML algorithms, support vector machine (SVM)…