@article{3347173,
    title = "ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey",
    author = "Bartsiokas, I.A. and Gkonis, P.K. and Kaklamani, D.I. and Venieris, I.S.",
    journal = "IEEE Access",
    year = "2022",
    volume = "10",
    pages = "83507-83528",
    publisher = "Institute of Electrical and Electronics Engineers, Inc. (IEEE)",
    issn = "2169-3536",
    doi = "10.1109/ACCESS.2022.3196657",
    keywords = "5G mobile communication systems;  Deep learning;  Learning algorithms;  Learning systems;  Millimeter waves;  MIMO systems;  Mobile edge computing;  Natural resources management;  Quality of service;  Radio transmission;  Resource allocation;  Surveys, 5g;  5g mobile communication;  B5G;  Deep learning;  Machine-learning;  MIMO communication;  Mobile communications;  NOMA;  Quality-of-service;  Radio resources managements;  Resource management, Wireless networks",
    abstract = "In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multiple-input-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail. © 2013 IEEE."
}