Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods

Abstract

The continuous growth of demand experienced by wireless networks creates a spectrum availability chal-lenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically con-gured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cogni-tive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learning-based schemes.