Key result
Machine learning applied to dynamic spectrum management provides a promising approach to improve spectral efficiency in wireless communication networks and the Internet of Things.
Why the study?
Conventional dynamic spectrum management relies on accurate network information or assumptions that are impractical in the Internet of Things, making machine learning applications promising.
This survey provides a comprehensive overview and taxonomy of machine learning applications in dynamic spectrum management for wireless networks.
May inform ML algorithm design for spectrum management; leaves open empirical validation in large-scale IoT networks.
Dynamic spectrum management (DSM) plays an increasingly important role in wireless communication networks for improving spectral efficiency. Conventionally, DSM is realized with the support of accurate information or with the dependence on assumptions about the network, which could be challenging and impractical in the Internet of Things where a large number of users need to be served. The application of machine learning into DSM is promising to address these issues, and many investigations have focused on this application. This article aims to survey the state-of-the-art research results along this direction. We devise a taxonomy to categorize the literature based on the operation modes, learning paradigms, enabling functions, and design objectives. Moreover, the key challenges are outlined to facilitate the application of machine learning for DSM. Finally, we present several open issues as the future research direction.
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Zhou et al. (2019) conducted a review in Dynamic spectrum management in wireless communication networks. Machine learning was evaluated. Machine learning applied to dynamic spectrum management provides a promising approach to improve spectral efficiency in wireless communication networks and the Internet of Things.
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