TY - JOUR
TI - ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey
AU - Bartsiokas, I.A.
AU - Gkonis, P.K.
AU - Kaklamani, D.I.
AU - Venieris, I.S.
JO - IEEE Access
PY - 2022
VL - 10
TODO - null
SP - 83507-83528
PB - Institute of Electrical and Electronics Engineers, Inc. (IEEE)
SN - 2169-3536
TODO - 10.1109/ACCESS.2022.3196657
TODO - 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
TODO - 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.
ER -