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Dynamic Spectrum Management via Machine Learning: State of the Art, Taxonomy, Challenges, and Open Research Issues

Lookup NU author(s): Zheng Chu

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Abstract

© 2012 IEEE.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.

Publication metadata

Author(s): Zhou F, Lu G, Wen M, Liang Y-C, Chu Z, Wang Y

Publication type: Article

Publication status: Published

Journal: IEEE Network

Year: 2019

Volume: 33

Issue: 4

Pages: 54-62

Online publication date: 31/07/2019

Acceptance date: 02/04/2019

ISSN (print): 0890-8044

ISSN (electronic): 1558-156X

Publisher: Institute of Electrical and Electronics Engineers Inc.

URL: https://doi.org/10.1109/MNET.2019.1800439

DOI: 10.1109/MNET.2019.1800439

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