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.

It is envisaged that in future Cognitive Radio (CR) networks deployment, multiple radio access networks may coexist. The networks may have different characteristics in terms of multiple attributes. CRs will have choices of selecting the optimal network out of the available networks. Optimal network selection is a challenging task that can be performed by spectrum handoff with Multiple Attribute Decision Making (MADM). The spectrum handoff decision with MADM provides wider and optimal choice with quality of service. This motivates the devolopment of a spectrum handoff scheme with MADM methods such as simple additive weighting, a technique for order preference by similarity to the ideal solution, a grey relational analysis and a cost function based method, which is the objective of this study. The CR preferences are based on voice, video and data services, called triple play services. The numerical results show that all MADM methods are effective for selecting the optimal network for spectrum handoff with a reduced complexity for the spectrum handoff decision. The paper shows that the proposed spectrum handoff scheme can be effectively implemented to select the optimal network according to triple play services in CR networks