Περίληψη:
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.
Συγγραφείς:
Bartsiokas, I.A.
Gkonis, P.K.
Kaklamani, D.I.
Venieris, I.S.
Λέξεις-κλειδιά:
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
