Modified fuzzy C-means and K-means clustering based spectrum sensing using cooperative spectrum for cognitive radio networks applications

Abstract

The recent decade has seen a rapid evolution of communication technologies and standards with the ultimate goal of providing global users with seamless connectivity a data access. Conventional methods of communication have been completely replaced by state-of-the-art hand-held gadgets and portable devices that enable users to communicate at high transmission rates. However, as high-end devices and gadgets become more popular and their demand for operating frequency which is essentially the Radio Frequency (RF) band in the EM (Electro Magnetic) spectrum tends to force the limits to the higher end of the RF spectrum. Due to the limitation of RF band availability, a spectrum is constructed for the requesting user for promising solution, and a difficult task. The emerging cognitive radio networks are a set of intelligent tools and scheme of identify the vacant spots in the band through effective sensing and allocating the spectrum to the requesting users. A modified cluster-based model has been proposed as part of extensive research on spectrum sensing. In the proposed work, a two-phase clustering model in the form of modified Fuzzy C-Means (FCM), and K-Means clustering is used, in which FCM is used as a training module on the channel features. K-Means is effectively used as an unsupervised classifier model. The proposed classification model was tested in a densely populated cognitive radio network compared to standard methods such as SVM (Support Vector Machine), FCM, and K-Means. Superior performance in terms of quality metrics like 90% classification accuracy, 91% spectral utility 90% are notable findings of this research work.