In this paper machine learning techniques based cooperative spectrum sensing (CSS)
algorithms are investigated for cognitive radio networks (CRN). A novel support vector …

Machine learning (ML) based classification methods have been viewed as one kind of
alternative solution for cooperative spectrum sensing (CSS) in recent years. In this paper …

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

Cognitive radio network (CRN) is one form of wireless communication for solving the
spectrum underutilization problem. It is mainly used for sensing and learning the …

Cognitive Radio is an intelligent wireless communication system able of learning from the
environment. It allows reusing of the radio resources available by users called Secondary …

In this work, the performance of support vector machine (SVM)-based classifier, applied for
spectrum sensing in cognitive radio (CR) networks, is analyzed. A single observation given …

Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To
develop methods of dynamic spectrum access, robust and efficient spectrum sensors are …

Spectrum sensing plays an important role in enabling cognitive radio technology for the up-
and-coming generation of wireless communication systems. Over the last decade, several …

Cognitive radio is an intelligent radio network that has advancement over the traditional
radio. The difference between the traditional and cognitive radio is that all the unused …

This study presents a new machine learning (support vector machine (SVM))‐based
cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to …