A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks

Due to exponential growth in demand for radio spectrum for wireless communication networking, the radio spectrum has become over-crowded. The fixed spectrum allocation policy of the radio spectrum leads to inefficient utilisation of the available spectrum, which diverted the attention of researchers towards different intelligent techniques to access the spectrum dynamically and efficiently. The concept of Cognitive Radio (CR) has been considered as a promising technology to solve the problem of spectrum scarcity through the utilisation of various unutilised spectrum bands. In a future network deployment, multiple radio access networks may coexist having different characteristics. Hence, it becomes a challenge for CR networks to select the optimal network out of available networks. For efficient realisation, CRs requires intelligent spectrum management techniques for Dynamic Spectrum Management (DSM). Till now, there does not exist a literature survey that addresses the spectrum management with machine learning techniques in an intelligent manner. Hence, this paper presents the detailed classification and comprehensive survey of various machine learning techniques for intelligent spectrum management with their paradigms of optimisation for cognitive radio networks. The paper also provides new directions and open issues for the research community to work further in CR networks.