LAUSR

One of the key foundations of 5th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.